Dissertations / Theses on the topic 'Learning statistics'
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Zhang, Bo. "Machine Learning on Statistical Manifold." Scholarship @ Claremont, 2017. http://scholarship.claremont.edu/hmc_theses/110.
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This senior thesis project explores and generalizes some fundamental machine learning algorithms from the Euclidean space to the statistical manifold, an abstract space in which each point is a probability distribution. In this thesis, we adapt the optimal separating hyperplane, the k-means clustering method, and the hierarchical clustering method for classifying and clustering probability distributions. In these modifications, we use the statistical distances as a measure of the dissimilarity between objects. We describe a situation where the clustering of probability distributions is needed and useful. We present many interesting and promising empirical clustering results, which demonstrate the statistical-distance-based clustering algorithms often outperform the same algorithms with the Euclidean distance in many complex scenarios. In particular, we apply our statistical-distance-based hierarchical and k-means clustering algorithms to the univariate normal distributions with k = 2 and k = 3 clusters, the bivariate normal distributions with diagonal covariance matrix and k = 3 clusters, and the discrete Poisson distributions with k = 3 clusters. Finally, we prove the k-means clustering algorithm applied on the discrete distributions with the Hellinger distance converges not only to the partial optimal solution but also to the local minimum.
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Thayne, Jeffrey L. "Making Statistics Matter: Using Self-data to Improve Statistics Learning." DigitalCommons@USU, 2016. https://digitalcommons.usu.edu/etd/5214.
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Research has demonstrated that well into their undergraduate and even graduate education, learners often struggle to understand basic statistical concepts, fail to see their relevance in their personal and professional lives, and often treat them as little more than mere mathematics exercises. This study explored ways help learners in an undergraduate learning context to treat statistical inquiry as mattering in a practical research context, by inviting them to ask questions about and analyze large, real, messy datasets that they have collected about their own personal lives (i.e., self-data). This study examined the conditions under which such an intervention might (and might not) successfully lead to a greater sense of the relevance of statistics to undergraduate learners.
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Choy, Ko-leung Tyrone. "An investigation on the learning of statistics with MINITAB." Hong Kong : University of Hong Kong, 1998. http://sunzi.lib.hku.hk/hkuto/record.jsp?B2005788X.
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Bonneau, Maxime. "Reinforcement Learning for 5G Handover." Thesis, Linköpings universitet, Statistik och maskininlärning, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-140816.
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The development of the 5G network is in progress, and one part of the process that needs to be optimised is the handover. This operation, consisting of changing the base station (BS) providing data to a user equipment (UE), needs to be efficient enough to be a seamless operation. From the BS point of view, this operation should be as economical as possible, while satisfying the UE needs. In this thesis, the problem of 5G handover has been addressed, and the chosen tool to solve this problem is reinforcement learning. A review of the different methods proposed by reinforcement learning led to the restricted field of model-free, off-policy methods, more specifically the Q-Learning algorithm. On its basic form, and used with simulated data, this method allows to get information on which kind of reward and which kinds of action-space and state-space produce good results. However, despite working on some restricted datasets, this algorithm does not scale well due to lengthy computation times. It means that the agent trained can not use a lot of data for its learning process, and both state-space and action-space can not be extended a lot, restricting the use of the basic Q-Learning algorithm to discrete variables. Since the strength of the signal (RSRP), which is of high interest to match the UE needs, is a continuous variable, a continuous form of the Q-learning needs to be used. A function approximation method is then investigated, namely artificial neural networks. In addition to the lengthy computational time, the results obtained are not convincing yet. Thus, despite some interesting results obtained from the basic form of the Q-Learning algorithm, the extension to the continuous case has not been successful. Moreover, the computation times make the use of reinforcement learning applicable in our domain only for really powerful computers.
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Wong, Sik-kwan Francis. "Outcome of a web-based statistic laboratory for teaching and learning of medical statistics." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B43251687.
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Saive, Yannick. "DirCNN: Rotation Invariant Geometric Deep Learning." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252573.
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Recently geometric deep learning introduced a new way for machine learning algorithms to tackle point cloud data in its raw form. Pioneers like PointNet and many architectures building on top of its success realize the importance of invariance to initial data transformations. These include shifting, scaling and rotating the point cloud in 3D space. Similarly to our desire for image classifying machine learning models to classify an upside down dog as a dog, we wish geometric deep learning models to succeed on transformed data. As such, many models employ an initial data transform in their models which is learned as part of a neural network, to transform the point cloud into a global canonical space. I see weaknesses in this approach as they are not guaranteed to perform completely invariant to input data transformations, but rather approximately. To combat this I propose to use local deterministic transformations which do not need to be learned. The novelty layer of this project builds upon Edge Convolutions and is thus dubbed DirEdgeConv, with the directional invariance in mind. This layer is slightly altered to introduce another layer by the name of DirSplineConv. These layers are assembled in a variety of models which are then benchmarked against the same tasks as its predecessor to invite a fair comparison. The results are not quite as good as state of the art results, however are still respectable. It is also my belief that the results can be improved by improving the learning rate and its scheduling. Another experiment in which ablation is performed on the novel layers shows that the layers main concept indeed improves the overall results.Nyligen har ämnet geometrisk deep learning presenterat ett nytt sätt för maskininlärningsalgoritmer att arbeta med punktmolnsdata i dess råa form.Banbrytande arkitekturer som PointNet och många andra som byggt på dennes framgång framhåller vikten av invarians under inledande datatransformationer. Sådana transformationer inkluderar skiftning, skalning och rotation av punktmoln i ett tredimensionellt rum. Precis som vi önskar att klassifierande maskininlärningsalgoritmer lyckas identifiera en uppochnedvänd hund som en hund vill vi att våra geometriska deep learning-modeller framgångsrikt ska kunna hantera transformerade punktmoln. Därför använder många modeller en inledande datatransformation som tränas som en del av ett neuralt nätverk för att transformera punktmoln till ett globalt kanoniskt rum. Jag ser tillkortakommanden i detta tillgångavägssätt eftersom invariansen är inte fullständigt garanterad, den är snarare approximativ. För att motverka detta föreslår jag en lokal deterministisk transformation som inte måste läras från datan. Det nya lagret i det här projektet bygger på Edge Convolutions och döps därför till DirEdgeConv, namnet tar den riktningsmässiga invariansen i åtanke. Lagret ändras en aning för att introducera ett nytt lager vid namn DirSplineConv. Dessa lager sätts ihop i olika modeller som sedan jämförs med sina efterföljare på samma uppgifter för att ge en rättvis grund för att jämföra dem. Resultaten är inte lika bra som toppmoderna resultat men de är ändå tillfredsställande. Jag tror även resultaten kan förbättas genom att förbättra inlärningshastigheten och dess schemaläggning. I ett experiment där ablation genomförs på de nya lagren ser vi att lagrens huvudkoncept förbättrar resultaten överlag.
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Sandberg, Martina. "Credit Risk Evaluation using Machine Learning." Thesis, Linköpings universitet, Statistik och maskininlärning, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-138968.
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In this thesis, we examine the machine learning models logistic regression, multilayer perceptron and random forests in the purpose of discriminate between good and bad credit applicants. In addition to these models we address the problem of imbalanced data with the Synthetic Minority Over-Sampling Technique (SMOTE). The data available have 273 286 entries and contains information about the invoice of the applicant and the credit decision process as well as information about the applicant. The data was collected during the period 2015-2017. With AUC-values at about 73%some patterns are found that can discriminate between customers that are likely to pay their invoice and customers that are not. However, the more advanced models only performed slightly better than the logistic regression.
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Vallin, Simon. "Small Cohort Population Forecasting via Bayesian Learning." Thesis, KTH, Matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209274.
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A set of distributional assumptions regarding the demographic processes of birth, death, emigration and immigration have been assembled to form a probabilistic model framework of population dynamics. This framework was summarized as a Bayesian network and Bayesian inference techniques are exploited to infer the posterior distributions of the model parameters from observed data. The birth, death and emigration processes are modelled using a hierarchical beta-binomial model from which the inference of the posterior parameter distribution was analytically tractable. The immigration process was modelled with a Poisson type regression model where posterior distribution of the parameters has to be estimated numerically. This thesis suggests an implementation of the Metropolis-Hasting algorithm for this task. Classifi cation of incomings into subpopulations of age and gender is subsequently made using a Dirichlet-multinomial hierarchic model, for which parameter inference is analytically tractable. This model framework is used to generate forecasts of demographic data, which can be validated using the observed outcomes. A key component of the Bayesian model framework used is that is estimates the full posterior distributions of demographic data, which can take into account the full amount of uncertainty when forecasting population growths.Genom att använda en mängd av distributionella antaganden om de demografiska processerna födsel, dödsfall, utflyttning och inflyttning har vi byggt ett stokastiskt ramverk för att modellera befolkningsförändringar. Ramverket kan sammanfattas som ett Bayesianskt nätverk och för detta nätverk introduceras tekniker för att skatta parametrar i denna uppsats. Födsel, dödsfall och utflyttning modelleras av en hierarkisk beta-binomialmodell där parametrarnas posteriorifördelning kan skattas analytiskt från data. För inflyttning används en regressionsmodell av Poissontyp där parametervärdenas posteriorifördelning måste skattas numeriskt. Vi föreslår en implementation av Metropolis-Hastingsalgoritmen för detta. Klassificering av subpopulationer hos de inflyttande sker via en hierarkisk Dirichlet-multinomialmodell där parameterskattning sker analytiskt. Ramverket användes för att göra prognoser för tidigare demografisk data, vilka validerades med de faktiska utfallen. En av modellens huvudsakliga styrkor är att kunna skatta en prediktiv fördelning för demografisk data, vilket ger en mer nyanserad pronos än en enkel maximum-likelihood-skattning.
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黃式鈞 and Sik-kwan Francis Wong. "Outcome of a web-based statistic laboratory for teaching and learning of medical statistics." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B43251687.
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RYSZ, TERI. "METACOGNITION IN LEARNING ELEMENTARY PROBABILITY AND STATISTICS." University of Cincinnati / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1099248340.
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Lundell, Jill F. "Tuning Hyperparameters in Supervised Learning Models and Applications of Statistical Learning in Genome-Wide Association Studies with Emphasis on Heritability." DigitalCommons@USU, 2019. https://digitalcommons.usu.edu/etd/7594.
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Machine learning is a buzz word that has inundated popular culture in the last few years. This is a term for a computer method that can automatically learn and improve from data instead of being explicitly programmed at every step. Investigations regarding the best way to create and use these methods are prevalent in research. Machine learning models can be difficult to create because models need to be tuned. This dissertation explores the characteristics of tuning three popular machine learning models and finds a way to automatically select a set of tuning parameters. This information was used to create an R software package called EZtune that can be used to automatically tune three widely used machine learning algorithms: support vector machines, gradient boosting machines, and adaboost.
The second portion of this dissertation investigates the implementation of machine learning methods in finding locations along a genome that are associated with a trait. The performance of methods that have been commonly used for these types of studies, and some that have not been commonly used, are assessed using simulated data. The affect of the strength of the relationship between the genetic code and the trait is of particular interest. It was found that the strength of this relationship was the most important characteristic in the efficacy of each method.
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Thayne, Jeffrey L. "Making statistics matter| Self-data as a possible means to improve statistics learning." Thesis, Utah State University, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10250713.
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Research has demonstrated that well into their undergraduate and even graduate education, learners often struggle to understand basic statistical concepts, fail to see their relevance in their personal and professional lives, and often treat them as little more than mere mathematics exercises. Undergraduate learners often see statistical concepts as means to passing exams, completing required courses, and moving on with their degree, and not as instruments of inquiry that can illuminate their world in new and useful ways.
This study explored ways help learners in an undergraduate learning context to treat statistical inquiry as mattering in a practical research context, by inviting them to ask questions about and analyze large, real, messy datasets that they have collected about their own personal lives (i.e., self -data). This study examined the conditions under which such an intervention might (and might not) successfully lead to a greater sense of the relevance of statistics to undergraduate learners. The goal is to place learners in a context where their relationship with data analysis can more closely mimic that of disciplinary professionals than that of students with homework; that is, where they are illuminating something about their world that concerns them for reasons beyond the limited concerns of the classroom.
The study revealed five themes in the experiences of learners working with self-data that highlight contexts in which data-analysis can be made to matter to learners (and how self-data can make that more likely): learners must be able to form expectations of the data, whether based on their own experiences or external benchmarks; the data should have variation to account for; the learners should treat the ups and downs of the data as more or less preferable in some way; the data should address or related to ongoing projects or concerns of the learner; and finally, learners should be able to investigate quantitative or qualitative covariates of their data. In addition, narrative analysis revealed that learners using self-data treated data analysis as more than a mere classroom exercise, but as exercises in inquiry and with an invested engagement that mimicked (in some ways) that of a disciplinary professional.
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Neykov, Matey. "Three Aspects of Biostatistical Learning Theory." Thesis, Harvard University, 2015. http://nrs.harvard.edu/urn-3:HUL.InstRepos:17467395.
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In the present dissertation we consider three classical problems in biostatistics and statistical learning - classification, variable selection and statistical inference.
Chapter 2 is dedicated to multi-class classification. We characterize a class of loss functions which we deem relaxed Fisher consistent, whose local minimizers not only recover the Bayes rule but also the exact conditional class probabilities. Our class encompasses previously studied classes of loss-functions, and includes non-convex functions, which are known to be less susceptible to outliers. We propose a generic greedy functional gradient-descent minimization algorithm for boosting weak learners, which works with any loss function in our class. We show that the boosting algorithm achieves geometric rate of convergence in the case of a convex loss. In addition we provide numerical studies and a real data example which serve to illustrate that the algorithm performs well in practice.
In Chapter 3, we provide insights on the behavior of sliced inverse regression in a high-dimensional setting under a single index model. We analyze two algorithms: a thresholding based algorithm known as diagonal thresholding and an L1 penalization algorithm - semidefinite programming, and show that they achieve optimal (up to a constant) sample size in terms of support recovery in the case of standard Gaussian predictors. In addition, we look into the performance of the linear regression LASSO in single index models with correlated Gaussian designs. We show that under certain restrictions on the covariance and signal, the linear regression LASSO can also enjoy optimal sample size in terms of support recovery. Our analysis extends existing results on LASSO's variable selection capabilities for linear models.
Chapter 4 develops general inferential framework for testing and constructing confidence intervals for high-dimensional estimating equations. Such framework has a variety of applications and allows us to provide tests and confidence regions for parameters estimated by algorithms such as the Dantzig Selector, CLIME and LDP among others, non of which has been previously equipped with inferential procedures.Biostatistics
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Sergue, Marie. "Customer Churn Analysis and Prediction using Machine Learning for a B2B SaaS company." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-269540.
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This past decade, the majority of services have been digitalized and data more and more available, easy to store and to process in order to understand customers behaviors. In order to be leaders in their proper industries, subscription-based businesses must focus on their Customer Relationship Management and in particular churn management, that is understanding customers cancelling their subscription. In this thesis, churn analysis is performed on real life data from a Software as a Service (SaaS) company selling an advanced cloud-based business phone system, Aircall. This use case has the particularity that the available dataset gathers customers data on a monthly basis and has a very imbalanced distribution of the target: a large majority of customers do not churn. Therefore, several methods are tried in order to diminish the impact of the imbalance while remaining as close as possible to the real world and the temporal framework. These methods include oversampling and undersampling (SMOTE and Tomek's link) and time series cross-validation. Then logistic regression and random forest models are used with an aim to both predict and explain churn.The non-linear method performed better than logistic regression, suggesting the limitation of linear models for our use case. Moreover, mixing oversampling with undersampling gives better performances in terms of precision/recall trade-off. Time series cross-validation also happens to be an efficient method to improve performance of the model. Overall, the resulting model is more useful to explain churn than to predict it. It highlighted some features majorly influencing churn, mostly related to product usage.Under det senaste decenniet har många tjänster digitaliserats och data blivit mer och mer tillgängliga, enkla att lagra och bearbeta med syftet att förstå kundbeteende. För att kunna vara ledande inom sina branscher måste prenumerationsbaserade företag fokusera på kundrelationshantering och i synnerhet churn management, det vill säga förståelse för hur kunder avbryter sin prenumeration. I denna uppsats utförs kärnanalys på verkliga data från ett SaaS-företag (software as a service) som säljer ett avancerat molnbaserat företagstelefonsystem, Aircall. Denna fallstudie är speciell på så sätt att den tillgängliga datamängden består av månatlig kunddata med en mycket ojämn fördelning: en stor majoritet av kunderna avbryter inte sina prenumerationer. Därför undersöks flera metoder för att minska effekten av denna obalans, samtidigt som de förblir så nära den verkliga världen och den tidsmässiga ramen. Dessa metoder inkluderar översampling och undersampling (SMOTE och Tomeks länk) och korsvalidering av tidsserier. Sedan används logistisk regression och random forests i syfte att både förutsäga och förklara prenumerationsbortfall. Den icke-linjära metoden presterade bättre än logistisk regression, vilket tyder på en begränsning hos linjära modeller i vårt användningsfall. Dessutom ger blandning av översampling med undersampling bättre prestanda när det gäller precision och återkoppling. Korsvalidering av tidsserier är också en effektiv metod för att förbättra modellens prestanda. Sammantaget är den resulterande modellen mer användbar för att förklara bortfall än att förutsäga dessa. Med hjälp av modellen kunde vissa faktorer, främst relaterade till produktanvändning, som påverkar bortfallet identifieras.
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Meister-Emerich, Keren A. "Analysis and evaluation of learning objects for use in an introductory statistics course." Laramie, Wyo. : University of Wyoming, 2008. http://proquest.umi.com/pqdweb?did=1594494281&sid=1&Fmt=2&clientId=18949&RQT=309&VName=PQD.
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Gardner, Kimberly D. "Investigating secondary school students' experience of learning statistics." unrestricted, 2007. http://etd.gsu.edu/theses/available/etd-12032007-153308/.
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Thesis (Ph. D.)--Georgia State University, 2007.Title from file title page. Christine Thomas, committee chair; Stephen Harmon, Pier Junor-Clark, Lynn Stallings, committee members. Electronic text (122 p.) : digital, PDF file. Description based on contents viewed August 11, 2008. Includes bibliographical references (p. 109-115).
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Gardner, Kimberly D. "Investigating Secondary School Students' Experience of Learning Statistics." Digital Archive @ GSU, 2008. http://digitalarchive.gsu.edu/msit_diss/30.
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Although more students are taking courses in statistics before leaving high school, the research base on teaching and learning statistics at the high school level has not accumulated as rapidly (Garfield & Chance, 2000). Very little is known about how secondary school students learn statistics, how the misconceptions they bring to the subject impede their learning, and what should be taught or assessed (Watson & Callingham, 2003). Studies that have investigated these issues tend to focus on the K-5, undergraduate, and graduate levels of education (Groth, 2003). Therefore, more research is needed at the secondary level (Garfield & Chance, 2000). The purpose of this qualitative investigation is to examine how secondary school students' approaches to learning relate to how they assign meaning to statistics. Phenomenography (Marton & Booth, 1997) is the theoretical orientation that frames the study, and it examines the role human experience plays in learning, by reporting variations in the ways participants experience a phenomenon (Dall'Alba & Hasselgreen, 1996). The research questions for the study were: 1) What are the different ways high school students define statistics? 2) What are the different ways high school students learn statistics? 3) What are the different ways students experience learning statistics? The nine participants in the study were high school graduates who completed a course in Statistics or Advanced Placement Statistics while enrolled in high school in a suburban area in the southeast. Data sources were semi-structured interviews and journaling. Using phenomenographic methodology, students' descriptions of the experience of learning were analyzed and coded. An outcome space of the collective experiences was constructed. A hierarchical relationship between students' approach to learning and their learning strategies was found. Also, a hierarchical relationship between students' approaches to learning and the meaning they assigned to statistics was found.
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O'Donohue, Michael G. "The teaching and learning of statistics in psychology." Thesis, Queen's University Belfast, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.286861.
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Mantooth, Renae. "Learning Spaces and Self-Efficacy in Undergraduate Statistics." UKnowledge, 2017. http://uknowledge.uky.edu/edp_etds/57.
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Learning environment research has typically focused on factors other than the physical environment (e.g., student/teacher relationships, organizational structure). This study investigated the relationship between the physical classroom environment and entry-level undergraduate statistics students’ (N = 844) academic beliefs and performance. Students were taught in either a technology-enhanced active learning classroom or a traditional lecture hall. This study investigated how undergraduate students in an entry level statistics course a) perceived the importance of the physical learning environment, b) conveyed expectations for and experiences of active engagement within that environment, and c) self-reported their personal capability judgments. Data were analyzed by examining mean differences, correlations, and regression. The nested data structure was accounted for using hierarchical linear modeling. Results indicated that, at the end of the semester, students rated the physical learning space as less important to their learning than they did at the beginning, although perceived importance was not influenced by classroom setting. The relationship between classroom type and active engagement expectation/experience offered mix results. Students learning in traditional classrooms reported higher statistics self-efficacy than did those in technology-enhanced statistics classrooms. End-of-course statistics self-efficacy was significantly related to grades earned.
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Smith, Tamarah. "Factors Related to Undergraduate Psychology Majors Learning Statistics." Diss., Temple University Libraries, 2013. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/216603.
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Educational PsychologyPh.D.
Factors Related to Undergraduate Psychology Majors Learning Statistics Tamarah Faye Smith Doctor of Philosophy: Educational Psychology Major Advisor: Dr. Frank Farley The American Psychological Association (APA) has outlined goals for psychology undergraduates. These goals are aimed at several objectives including the need to build skills for interpreting and conducting psychological research (APA, 2007). These skills allow psychologists to conduct research that is covered in the media (Farley et al. 2009) and influences policy and law (Fischer, Stein & Heikkinen, 2009; Steinberg, Cauffman, Woolard, Graham & Banich, 2009a; Steinberg, Cauffman, Woolard, Graham & Banich, 2009b). One of the fundamental courses required for building these skills is statistics, a course that begins at the undergraduate level. Research has suggested that performance after completing statistics courses is weak for many students (Garfield, 2003; Hirsch & O'Donnell, 2001; Konold et al. 1993; Mulhern & Wylie, 2005; Schau & Mattern, 1997). The current study examined factors that may be related to performance on a statistical test. A sample of 231 students enrolled in or having already completed a statistics course for psychology majors completed a statistical skill questionnaire, built by the author, to measure performance with four APA outlined goals. To measure student attitudes the Survey of Attitudes Toward Statistics (SATS-36; Schau, 2003) was completed with adapted questions to measure perceived attitudes of peers and faculty toward statistics. Finally, questions pertaining to classroom techniques and content areas covered were assessed. Building off of social cognitive theory (SCT; Bandura, 1986) and expectancy-value theory (Eccles & Wigfield, 2002), it was expected that lower attitudes, such as low value and low interest, among the students and those perceived to be held by faculty and peers would be related to lower performance on the statistical test. A series of linear regressions were conducted and revealed no significant relationship between perceived faculty attitudes and performance. Students' own liking and positive affect ratings were positive predictors of performance indicating a gain of 3-4% on the statistical test. However, an interesting negative relationship emerged with respect to students' value of statistics and peer interest scores where performance on the statistical test decreased as value and peer interest increased. This may be demonstrating issues pertaining to the SATS-36 validity when measuring students' value as well as issues with the items created to measure perceived peer interest. The results of a factor analysis on perceived attitude measures for peers and faculty suggest that the need for more items is necessary, particularly for faculty attitudes. Finally, this study provides a first look at the performance of a sample of psychology students with APA goals for quantitative reasoning. Results showed that students performed best at reading basic descriptive statistics (M=74.5%), and worst when choosing statistical tests for a given research hypothesis (M=30%). Performance on questions pertaining to confidence intervals (M=38%) and discriminating between statistical and practical significance (M=39%) was also low. Future research can address limitations of this study by expanding the sample to include a broader range of psychology undergraduates and including additional items for measuring perceived attitudes. Other methodological approaches, such as experimental design and directly measuring faculty attitudes, should also be considered. Finally, further research and replication are necessary to determine if scores on the statistical test will continue to be low with other samples and varying question formats. These results can then be used to generate conversation about why and how students are, or are not, learning the appropriate quantitative skills.
Temple University--Theses
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Gordon, Susan Eve. "Understanding Students Learning Statistics: An Activity Theory Approach." Thesis, The University of Sydney, 1998. http://hdl.handle.net/2123/353.
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In this project I investigate university students orientations to learning statistics. The students who participated in my research were studying statistics as a compulsory component of their psychology course. My central thesis is that learning develops in the relationship between the thinking, feeling and acting person and the social, institutional and cultural contexts surrounding him or her. How students orient themselves or position themselves to learn statistics is reflected in their engagement with the learning task and their activities. These activities determine the quality of their learning and emerging knowledge. To understand student learning I draw on the powerful theories of Vygotsky (1962, 1978) and Leontev (1978, 1981). In particular, I extend and apply Leontev's construct of activity (Leontev, 1981). This suggests that individuals act in accordance with their purposes and needs which are shaped by and reflect histories and resources, both personal and cultural. My investigation consists of two studies. Study One is a qualitative exploration of the orientations to learning statistics of five older students. These students sought help with statistics at the Mathematics Learning Centre where I work. My case studies of these students are inseparable from my efforts to help them learn statistics. Study Two is grounded in Study One. The main source of data for this broader study is a survey which was completed by 279 psychology students studying statistics. In keeping with the theoretical framework, my methodology involves a holistic analysis of students and the milieu in which they act. My findings suggest relationships among students affective appraisals; their conceptions of statistics; their approaches to learning it; their evaluations and the outcomes of their actions. In Study One the relationships emerged from the students' descriptions. In Study Two I quantified the ways in which variables related to each other. Structure for the data was provided by means of correlations, factor analysis and cluster analysis. For this study I also interviewed students and teachers of statistics. My data support the systemic view of teaching and learning in context afforded by my theoretical perspective. Learning statistics involves the whole person (Semenov, 1978) and is inseparable from the arena of his or her actions. The goal of statistics education is surely to enable students to develop useful, meaningful knowledge. My findings suggest that for many of the participants in my project this goal was not being met. Most of these students reported their reluctance to learn statistics and described adopting primarily surface approaches to learning it. A range of conceptions of the subject was expressed, but for many of the students statistical meaning was evidently reduced to performance on assessment tasks. Such orientations to learning statistics may lead to it becoming irrelevant and inert information. For a few students, however, the experience of learning statistics led to self development and enhanced perspectives on the world in which we live. My project indicates the diversity of students' experiences. It raises issues as to why we teach statistics today and how the teaching and learning of statistics is being supported at university. //REFERENCES Leontev, A. N. (1978). Activity, Consciousness, and Personality. (M. J. Hall, Trans.). Englewood Cliffs, New Jersey: Prentice-Hall. Leontev, A. N. (1981). The problem of activity in psychology. In J. V. Wertsch (Ed.), The Concept of Activity in Soviet Psychology, (pp. 37-71). New York: M. E. Sharpe. Semenov, N. (1978). An empirical psychological study of thought processes in creative problem-solving from the perspective of the theory of activity. Soviet Psychology, 16(1), 3-46. Vygotsky, L. S. (1962). Thought and Language. Cambridge, Massachusetts: The M.I.T. Press. Vygotsky, L. S. (1978). Mind in Society. Cambridge, MA: Harvard University Press.
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Gordon, Susan Eve. "Understanding Students Learning Statistics: An Activity Theory Approach." University of Sydney. School of Development and Learning, 1998. http://hdl.handle.net/2123/353.
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In this project I investigate university students orientations to learning statistics. The students who participated in my research were studying statistics as a compulsory component of their psychology course. My central thesis is that learning develops in the relationship between the thinking, feeling and acting person and the social, institutional and cultural contexts surrounding him or her. How students orient themselves or position themselves to learn statistics is reflected in their engagement with the learning task and their activities. These activities determine the quality of their learning and emerging knowledge. To understand student learning I draw on the powerful theories of Vygotsky (1962, 1978) and Leontev (1978, 1981). In particular, I extend and apply Leontev's construct of activity (Leontev, 1981). This suggests that individuals act in accordance with their purposes and needs which are shaped by and reflect histories and resources, both personal and cultural. My investigation consists of two studies. Study One is a qualitative exploration of the orientations to learning statistics of five older students. These students sought help with statistics at the Mathematics Learning Centre where I work. My case studies of these students are inseparable from my efforts to help them learn statistics. Study Two is grounded in Study One. The main source of data for this broader study is a survey which was completed by 279 psychology students studying statistics. In keeping with the theoretical framework, my methodology involves a holistic analysis of students and the milieu in which they act. My findings suggest relationships among students affective appraisals; their conceptions of statistics; their approaches to learning it; their evaluations and the outcomes of their actions. In Study One the relationships emerged from the students' descriptions. In Study Two I quantified the ways in which variables related to each other. Structure for the data was provided by means of correlations, factor analysis and cluster analysis. For this study I also interviewed students and teachers of statistics. My data support the systemic view of teaching and learning in context afforded by my theoretical perspective. Learning statistics involves the whole person (Semenov, 1978) and is inseparable from the arena of his or her actions. The goal of statistics education is surely to enable students to develop useful, meaningful knowledge. My findings suggest that for many of the participants in my project this goal was not being met. Most of these students reported their reluctance to learn statistics and described adopting primarily surface approaches to learning it. A range of conceptions of the subject was expressed, but for many of the students statistical meaning was evidently reduced to performance on assessment tasks. Such orientations to learning statistics may lead to it becoming irrelevant and inert information. For a few students, however, the experience of learning statistics led to self development and enhanced perspectives on the world in which we live. My project indicates the diversity of students' experiences. It raises issues as to why we teach statistics today and how the teaching and learning of statistics is being supported at university. //REFERENCES Leontev, A. N. (1978). Activity, Consciousness, and Personality. (M. J. Hall, Trans.). Englewood Cliffs, New Jersey: Prentice-Hall. Leontev, A. N. (1981). The problem of activity in psychology. In J. V. Wertsch (Ed.), The Concept of Activity in Soviet Psychology, (pp. 37-71). New York: M. E. Sharpe. Semenov, N. (1978). An empirical psychological study of thought processes in creative problem-solving from the perspective of the theory of activity. Soviet Psychology, 16(1), 3-46. Vygotsky, L. S. (1962). Thought and Language. Cambridge, Massachusetts: The M.I.T. Press. Vygotsky, L. S. (1978). Mind in Society. Cambridge, MA: Harvard University Press.
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Lindberg, Jesper. "Simulation driven reinforcement learning : Improving synthetic enemies in flight simulators." Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166593.
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This project focuses on how to implement an Artificial Intelligence (AI) -agent in a Tactical Simulator (Tacsi). Tacsi is a simulator used by Saab AB, one thing that the simulator is used for is pilot training. In this work, Tacsi will be used to simulate air to air combat. The agent uses Reinforcement Learning (RL) to be able to explore and learn how the simulator behaves. This knowledge will then be exploited when the agent tries to beat a computer-controlled synthetic enemy. The result of this study may be used to produce better synthetic enemies for pilot training. The RL-algorithm used in this work is deep Q-Learning, a well-known algorithm in the field. The results of the work show that it is possible to implement an RL-agent in Tacsi which can learn from the environment and be able to defeat the enemy, in some scenarios. The result produced by the algorithm verified that a RL-Agent works within Tacsi at Saab AB. Although the performance of the agent in this work is not impressive, there is a great opportunity for further development of the agent as well as the working environment.
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Rydén, Otto. "Statistical learning procedures for analysis of residential property price indexes." Thesis, KTH, Matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-207946.
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Residential Price Property Indexes (RPPIs) are used to study the price development of residential property over time. Modeling and analysing an RPPI is not straightforward due to residential property being a heterogeneous good. This thesis focuses on analysing the properties of the two most conventional hedonic index modeling approaches, the hedonic time dummy method and the hedonic imputation method. These two methods are analysed with statistical learning procedures from a regression perspective, specifically, ordinary least squares regression, and a number of more advanced regression approaches, Huber regression, lasso regression, ridge regression and principal component regression. The analysis is based on the data from 56 000 apartment transactions in Stockholm during the period 2013-2016 and results in several models of a RPPI. These suggested models are then validated using both qualitative and quantitative methods, specifically a bootstrap re-sampling to perform analyses of an empirical confidence interval for the index values and a mean squared errors analysis of the different index periods. Main results of this thesis show that the hedonic time dummy index methodology produces indexes with smaller variances and more robust indexes for smaller datasets. It is further shown that modeling of RPPIs with robust regression generally results in a more stable index that is less affected by outliers in the underlying transaction data. This type of robust regression strategy is therefore recommended for a commercial implementation of an RPPI.Bostadsprisindex används för att undersöka prisutvecklingen för bostäder över tid. Att modellera ett bostadsprisindex är inte alltid lätt då bostäder är en heterogen vara. Denna uppsats analyserar skillnaden mellan de tvåhuvudsakliga hedoniska indexmodelleringsmetoderna, som är, hedoniska tiddummyvariabelmetoden och den hedoniska imputeringsmetoden. Dessa metoder analyseras med en statistisk inlärningsprocedur gjord utifrån ett regressionsperspektiv, som inkluderar analys utav minsta kvadrats-regression, Huberregression, lassoregression, ridgeregression och principal componentregression. Denna analys är baserad på ca 56 000 lägenhetstransaktioner för lägenheter i Stockholm under perioden 2013-2016 och används för att modellera era versioner av ett bostadsprisindex. De modellerade bostadsprisindexen analyseras sedan med hjälp utav både kvalitativa och kvantitativa metoder inklusive en version av bootstrap för att räkna ut ett empiriskt konfidensintervall för bostadsprisindexen samt en medelfelsanalys av indexpunktskattningarna i varje tidsperiod. Denna analys visar att den hedoniska tid-dummyvariabelmetoden producerar bostadsprisindex med mindre varians och ger också robustare bostadsprisindex för en mindre datamängd. Denna uppsats visar också att användandet av robustare regressionsmetoder leder till stabilare bostadsprisindex som är mindre påverkade av extremvärden, därför rekommenderas robusta regressionsmetoder för en kommersiell implementering av ett bostadsprisindex.
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Enver, Asad. "Modeling Trouble Ticket ResolutionTime Using Machine Learning." Thesis, Linköpings universitet, Statistik och maskininlärning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176779.
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This thesis work, conducted at Telenor Sweden, aims to build a model that would try to accurately predict the resolution time of Priority 4 Trouble Tickets. (Priority 4 trouble tickets are those tickets that get generated more often-e in higher volumes per month). It explores and investigates the possibility of applying Machine Learning and Deep Learning techniques to trouble ticket data to find an optimal solution that performs better than the current method in place (which is explained in Section 3.5). The model would be used by Telenor to inform the end-users of when the networks team expects to resolve the issues that are affecting them.
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Rowan, Adriaan. "Unravelling black box machine learning methods using biplots." Master's thesis, Faculty of Science, 2019. http://hdl.handle.net/11427/31124.
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Following the development of new mathematical techniques, the improvement of computer processing power and the increased availability of possible explanatory variables, the financial services industry is moving toward the use of new machine learning methods, such as neural networks, and away from older methods such as generalised linear models. However, their use is currently limited because they are seen as “black box” models, which gives predictions without justifications and which are therefore not understood and cannot be trusted. The goal of this dissertation is to expand on the theory and use of biplots to visualise the impact of the various input factors on the output of the machine learning black box. Biplots are used because they give an optimal two-dimensional representation of the data set on which the machine learning model is based.The biplot allows every point on the biplot plane to be converted back to the original ��-dimensions – in the same format as is used by the machine learning model. This allows the output of the model to be represented by colour coding each point on the biplot plane according to the output of an independently calibrated machine learning model. The interaction of the changing prediction probabilities – represented by the coloured output – in relation to the data points and the variable axes and category level points represented on the biplot, allows the machine learning model to be globally and locally interpreted. By visualing the models and their predictions, this dissertation aims to remove the stigma of calling non-linear models “black box” models and encourage their wider application in the financial services industry.
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Leong, Jennifer. "High school students' attitudes and beliefs regarding statistics in a service-learning-based statistics course." unrestricted, 2006. http://etd.gsu.edu/theses/available/etd-11292006-140510/.
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Thesis (Ph. D.)--Georgia State University, 2006.Title from title screen. Christine Thomas, committee chair; Joel Meyers, Draga Vidakovic, Steve Harmon, committee members. Electronic text (196 p.) : digital, PDF file. Description based on contents viewed July 31, 2007. Includes bibliographical references (p. 154-169).
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Hild, Andreas. "ESTIMATING AND EVALUATING THE PROBABILITY OF DEFAULT – A MACHINE LEARNING APPROACH." Thesis, Uppsala universitet, Statistiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447385.
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In this thesis, we analyse and evaluate classification models for panel credit risk data. Variables are selected based on results from recursive feature elimination as well as economic reasoning where the probability of default is estimated. We employ several machine learning and statistical techniques and assess the performance of each model based on AUC, Brier score as well as the absolute mean difference between the predicted and the actual outcome, carried out with cross validation of four folds and extensive hyperparameter optimization. The LightGBM model had the best performance and many machine learning models showed a superior performance compared to traditional models like logistic regression. Hence, the results of this thesis show that machine learning models like gradient boosting models, neural networks and voting models have the capacity to challenge traditional statistical methods such as logistic regression within credit risk modelling.
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Hedblom, Edvin, and Rasmus Åkerblom. "Debt recovery prediction in securitized non-performing loans using machine learning." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252311.
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Credit scoring using machine learning has been gaining attention within the research field in recent decades and it is widely used in the financial sector today. Studies covering binary credit scoring of securitized non-performing loans are however very scarce. This paper is using random forest and artificial neural networks to predict debt recovery for such portfolios. As a performance benchmark, logistic regression is used. Due to the nature of high imbalance between the classes, the performance is evaluated mainly on the area under both the receiver operating characteristic curve and the precision-recall curve. This paper shows that random forest, artificial neural networks and logistic regression have similar performance. They all indicate an overall satisfactory ability to predict debt recovery and hold potential to be implemented in day-to-day business related to non-performing loans.Bedömning av kreditvärdighet med maskininlärning har fått ökad uppmärksamhet inom forskningen under de senaste årtiondena och är ofta använt inom den finansiella sektorn. Tidigare studier inom binär klassificering av kreditvärdighet för icke-presterande lånportföljer är få. Denna studie använder random forest och artificial neural networks för att prediktera återupptagandet av lånbetalningar för sådana portföljer. Som jämförelse används logistisk regression. På grund av kraftig obalans mellan klasserna kommer modellerna att bedömas huvudsakligen på arean under reciever operating characteristic-kurvan och precision-recall-kurvan. Denna studie visar på att random forest, artificial neural networks och logistisk regression presterar likartat med överlag goda resultat som har potential att fördelaktigt implementeras i praktiken.
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Mirzaikamrani, Sonya. "Predictive modeling and classification for Stroke using the machine learning methods." Thesis, Örebro universitet, Handelshögskolan vid Örebro Universitet, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-81837.
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Gold, David L. "Bayesian learning in bioinformatics." [College Station, Tex. : Texas A&M University, 2007. http://hdl.handle.net/1969.1/ETD-TAMU-1624.
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Ziegenhagen, Uwe. "Essays on the use of e-Learning in statistics and the implementation of statistical software." Doctoral thesis, Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät, 2009. http://dx.doi.org/10.18452/15914.
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Die vorliegende Doktorarbeit bündelt die Veröffentlichungen des Autors und seiner Koautoren zu den Themen e-Learning und statistischer Software. Die Kapitel 2 bis 5 sind Aspekten des e-Learning gewidmet, die Kapitel 6 bis 9 beschreiben die Entwicklung der statistischen Programmiersprache Yxilon. In Kapitel 2, Koautoren Wolfgang Härdle und Sigbert Klinke, wird erörtert, ob und wie computerbasierte Elemente in den Kanon der methodischen Bildung integriert werden sollen und wo die Grenzen des e-Learning in der Statistik-Ausbildung liegen. Kapitel 3, Koautoren Wolfgang Härdle und Sigbert Klinke, gibt Einschätzungen verschiedener e-Learning Plattformen und beschreibt Punkte, die bei der Entwicklung von e-Learning Plattformen berücksichtigt werden sollten. Kapitel 4, geschrieben mit Wolfgang Härdle und Sigbert Klinke, diskutiert zwei Veröffentlichungen in der "International Statistical Review", die eine technische Lösung für die Verbesserung des Verständnisses der Statistik-Lehre vorstellen. Kapitel 5, Koautoren Wolfgang Härdle und Sigbert Klinke, beschreibt die Anwendung von Web-Techniken für die Lehre in Statistik. Weiterhin stellt es die Quantnet Plattform vor, eine Plattform für die Verwaltung von Programmen und Daten. In Kapitel 6, Koautoren Wolfgang Härdle und Sigbert Klinke, diskutieren die Autoren die Anforderungen an eine Statistical Engine. Kapitel 7, geschrieben mit Yuval Guri und Sigbert Klinke, erläutert die Ideen, die zur Re-Implementierung der XploRe Sprache geführt haben und diskutiert ausgewählte technische Aspekte der Yxilon Plattform wie Objektdatenbank und die Erzeugung von kompilierbarem Code für Hochsprachen. In Kapitel 8, Koautoren Wolfgang Härdle und Sigbert Klinke, wird die implementierte Client-Server Struktur beschrieben. Server und Kommunikationsprotokoll werden zusammen mit dem entwickelten Client und der Grafik-Engine beschrieben. Das letzte Kapitel, beschreibt die Struktur der Yxilon Plattform in ihrer jetzigen Form.The following doctoral thesis collects the papers the author has written with his coauthors on e-Learning and statistical software. The chapters 2 to 5 are devoted to selected aspects of e-Learning, the chapters 6 to 9 describe the development of the statistical programming environment Yxilon. In chapter 2, coauthored by Wolfgang Härdle and Sigbert Klinke, the question whether and how computational elements should be integrated into the canon of methodological education and where e-techniques have their limits in statistics education is discussed. Chapter 3, coauthored by Wolfgang Härdle and Sigbert Klinke, gives reviews of different e-learning platforms for statistics and reveals facts that may be taken into account for future e-learning platforms in statistics and related fields. Chapter 4, written with Wolfgang Härdle and Sigbert Klinke, discusses two papers published in International Statistical Review which both offer a technical solution to improve the understanding of statistics by students. Chapter 5, coauthored by Wolfgang Härdle and Sigbert Klinke, describes web-related techniques for teaching statistics. It furthermore introduces the Quantnet platform, a framework to manage scientific code and data. In chapter 6, coauthored by Wolfgang Härdle and Sigbert Klinke, the requirements for a statistical engine are discussed. Chapter 7, written jointly with Yuval Guri and Sigbert Klinke, explains ideas which led to the reimplementation of the XploRe language. In chapter 8, coauthored by Wolfgang Härdle and Sigbert Klinke, the implemented client/server structure of the Yxilon platform is laid out in terms of technical features. The server and the communication protocol are described together with the developed Java client featuring the Jasplot graphics engine. Finally chapter 9 describes the structure of the Yxilon environment in its present form.
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Leong, Jennifer. "High School Students' Attitudes and Beliefs Regarding Statistics in a Service-Learning-Based Statistics Course." Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/msit_diss/12.
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Despite agreement among researchers about the powerful influence of attitudes and beliefs on the development of students’ mathematical knowledge base (Leder, Pehkonen, & Törner, 2002), relatively little is known about these constructs in statistics education. This study investigated the relationship between mathematics-and statistics-related attitudes and beliefs of 11 high school students in an introductory statistics course designed around a 13-week long service-learning project. Service-learning is a pedagogical approach that situates academic learning in the context of community service. The study utilized qualitative, teacher-researcher (Cochran-Smith & Lytle, 1993) methodology from an interpretivist perspective. The three primary modes of data collection were journals, narratives, and an open-ended survey (Survey of Mathematical and Statistical Affect). Observations and reflections were also recorded regularly in a researcher journal. Inquiry adhered to guidelines for trustworthiness and rigor as outlined by Lincoln and Guba (1985). Item, pattern, and structural levels of analysis were employed (LeCompte and Schensul, 1999b). Investigation into attitudes and beliefs was framed in accordance with Op t’ Eynde, De Corte, and Verschaffel’s (2002) conceptualization of the mathematics-related belief system and McLeod’s (1992) framework of the affective domain in mathematics education. Results indicate that participants’ attitudes toward mathematics and statistics tended to converge while participants’ beliefs regarding mathematics and statistics tended to diverge. Participants like mathematics and statistics that involve real-life scenarios. Participants also like mathematics and statistics that do not require complex mathematical tasks. Participants’ beliefs regarding statistics were generally more positive than beliefs regarding mathematics. Participants reported greater confidence doing statistics than mathematics and contribute this confidence, in part, to service-learning. Participants also experienced a heightened sense of social awareness and social responsibility through the service-learning project. These results provide evidence that service-learning can be utilized to solidify positive attitudes and beliefs regarding statistics among high school students, in spite of potentially less positive ones toward mathematics.
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Ekdahl, Magnus. "Approximations of Bayes Classifiers for Statistical Learning of Clusters." Licentiate thesis, Linköping : Linköpings universitet, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-5856.
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Lu, Yu. "Statistical and Computational Guarantees for Learning Latent Variable Models." Thesis, Yale University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10783452.
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Latent variable models are widely used to capture the underlying structures of the data, for example, Gaussian mixture models for speech recognition, stochastic block models for community detection and topic models for information retrieval. While alternative minimization based algorithms such as EM algorithm and Lloyd's algorithm performs well in practice, there has been little theoretical advancement in explaining the effectiveness of these algorithms. In this thesis, we investigate the performance of Lloyd's algorithm and EM algorithm on clustering two-mixture of Gaussians. With an initializer slightly better than random guess, we are able to show the linear converge of Lloyd's and EM iterations to the statistical optimal estimator. These results shed light on the global convergence of more general non-convex optimizations.
We generalized the results to arbitrary number of sub-Gaussian mixtures. Motivated by the Lloyd's algorithm, we propose new algorithms for other latent variable models including sparse gaussian mixture model, stochastic block model. biclustering model and Dawid-Skene model. The proposed algorithms are computationally efficient and shown to be rate-optimal under mild signal-to-noise ratio conditions. The highlight of our theoretical analysis is to develop new proof techniques to handle the dependency between iterations, which can be applied to other iterative algorithms with explicit iteration formulas.
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Kunz, Matthew Ross. "Fused Lasso and Tensor Covariance Learning with Robust Estimation." Thesis, The Florida State University, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=10973227.
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With the increase in computation and data storage, there has been a vast collection of information gained with scientific measurement devices. However, with this increase in data and variety of domain applications, statistical methodology must be tailored to specific problems. This dissertation is focused on analyzing chemical information with an underlying structure.
Robust fused lasso leverages information about the neighboring regression coefficient structure to create blocks of coefficients. Robust modifications are made to the mean to account for gross outliers in the data. This method is applied to near infrared spectral measurements in prediction of an aqueous analyte concentration and is shown to improve prediction accuracy.
Expansion on the robust estimation and structure analysis is performed by examining graph structures within a clustered tensor. The tensor is subjected to wavelet smoothing and robust sparse precision matrix estimation for a detailed look into the covariance structure. This methodology is applied to catalytic kinetics data where the graph structure estimates the elementary steps within the reaction mechanism.
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Berlin, Daniel. "Multi-class Supervised Classification Techniques for High-dimensional Data: Applications to Vehicle Maintenance at Scania." Thesis, KTH, Matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209257.
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In vehicle repairs, many times locating the cause of error could turn out more time consuming than the reparation itself. Hence a systematic way to accurately predict a fault causing part would constitute a valuable tool especially for errors difficult to diagnose. This thesis explores the predictive ability of Diagnostic Trouble Codes (DTC’s), produced by the electronic system on Scania vehicles, as indicators for fault causing parts. The statistical analysis is based on about 18800 observations of vehicles where both DTC’s and replaced parts could be identified during the period march 2016 - march 2017. Two different approaches of forming classes is evaluated. Many classes had only few observations and, to give the classifiers a fair chance, it is decided to omit observations of classes based on their frequency in data. After processing, the resulting data could comprise 1547 observations on 4168 features, demonstrating very high dimensionality and making it impossible to apply standard methods of large-sample statistical inference. Two procedures of supervised statistical learning, that are able to cope with high dimensionality and multiple classes, Support Vector Machines and Neural Networks are exploited and evaluated. The analysis showed that on data with 1547 observations of 4168 features (unique DTC’s) and 7 classes SVM yielded an average prediction accuracy of 79.4% compared to 75.4% using NN.The conclusion of the analysis is that DTC’s holds potential to be used as indicators for fault causing parts in a predictive model, but in order to increase prediction accuracy learning data needs improvements. Scope for future research to improve and expand the model, along with practical suggestions for exploiting supervised classifiers at Scania is provided. keywords: Statistical learning, Machine learning, Neural networks, Deep learning, Supervised learning, High dimensionalityMånga gånger i samband med fordonsreparationer är felsökningen mer tidskrävande än själva reparationen. Således skulle en systematisk metod för att noggrant prediktera felkällan vara ett värdefullt verktyg för att diagnostisera reparationsåtgärder. I denna uppsats undersöks möjligheten att använda Diagnostic Trouble Codes (DTC:er), som genereras av de elektroniska systemen i Scanias fordon, som indikatorer för att peka ut felorsaken. Till grund för analysen användes ca 18800 observationer av fordon där både DTC:er samt utbytta delar kunnat identifieras under perioden mars 2016 - mars 2017. Två olika strategier för att generera klasser har utvärderats. Till många av klasserna fanns det endast ett fåtal observationer, och för att ge de prediktiva modellerna bra förutsättningar så användes endast klasser med tillräckligt många observationer i träningsdata. Efter bearbetning kunde data innehålla 1547 observationer 4168 attribut, vilket demonstrerar problemets höga dimensionalitet och gör det omöjligt att applicera standard metoder för statistisk analys på stora datamängder. Två metoder för övervakad statistisk inlärning, lämpliga för högdimensionell data med multipla klasser, Södvectormaskiner (SVM) samt Neurala Nätverk (NN) implementeras och deras resultat utvärderas. Analysen visade att på data med 1547 observationer av 4168 attribut (unika DTC:er) och 7 klasser kunde SVM prediktera observationer till klasserna med 79.4% noggrannhet jämfört med 75.4% för NN. De slutsatser som kunde dras av analysen var att DTC:er tycks ha potential att användas för att indikera felorsaker med en prediktiv modell, men att den data som ligger till grund för analysen bör förbättras för att öka noggrannheten i de prediktiva modellerna. Framtida forskningsmöjligheter för att ytterligare förbättra samt utveckla modellen, tillsammans med förslag för hur övervakade klassificerings modeller kan användas på Scnaia har identifierats.
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Ramey, James M. "Differences in Statistical Reasoning Abilities through Behavioral-Cognitive Combinations of Videos and Formative Assessments in Undergraduate Statistics Courses." Digital Commons @ East Tennessee State University, 2015. https://dc.etsu.edu/etd/2494.
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This study evaluated whether significant differences in statistical reasoning abilities exist for completers of short online instructional videos and formative quizzes for students in undergraduate introductory statistics courses. Data for the study were gathered during the Fall 2013 semester at a community college in Northeast Tennessee.
Computer-based pedagogical tools can promote improved conceptual reasoning ability (Trumpower & Sarwar, 2010; Van der Merwe, 2012). Additionally, prior research demonstrated a significant relationship between formative quiz access and student achievement (Stull, Majerich, Bernacki, Varnum, & Ducette, 2011; Wilson, Boyd, Chen, & Jamal, 2011), as well as multimedia object access and student achievement (Bliwise, 2005; Miller, 2013). Four research questions were used to guide the study. A series of analysis of variance (ANOVA) statistical procedures was used to analyze the data.
Findings indicated no significant differences in statistical reasoning abilities between students who were provided access to supplemental online instructional videos and formative quizzes and students who were not provided access. Moreover, statistical reasoning abilities did not differ significantly based upon number of quizzes successfully completed, average number of quiz attempts, or number of videos accessed.
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Fuglesang, Rutger. "Particle-Based Online Bayesian Learning of Static Parameters with Application to Mixture Models." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279847.
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This thesis investigates the possibility of using Sequential Monte Carlo methods (SMC) to create an online algorithm to infer properties from a dataset, such as unknown model parameters. Statistical inference from data streams tends to be difficult, and this is particularly the case for parametric models, which will be the focus of this paper. We develop a sequential Monte Carlo algorithm sampling sequentially from the model's posterior distributions. As a key ingredient of this approach, unknown static parameters are jittered towards the shrinking support of the posterior on the basis of an artificial Markovian dynamic allowing for correct pseudo-marginalisation of the target distributions. We then test the algorithm on a simple Gaussian model, a Gausian Mixture Model (GMM), as well as a variable dimension GMM. All tests and coding were done using Matlab. The outcome of the simulation is promising, but more extensive comparisons to other online algorithms for static parameter models are needed to really gauge the computational efficiency of the developed algorithm.Detta examensarbete undersöker möjligheten att använda Sekventiella Monte Carlo metoder (SMC) för att utveckla en algoritm med syfte att utvinna parametrar i realtid givet en okänd modell. Då statistisk slutledning från dataströmmar medför svårigheter, särskilt i parameter-modeller, kommer arbetets fokus ligga i utvecklandet av en Monte Carlo algoritm vars uppgift är att sekvensiellt nyttja modellens posteriori fördelningar. Resultatet är att okända, statistiska parametrar kommer att förflyttas mot det krympande stödet av posterioren med hjälp utav en artificiell Markov dynamik, vilket tillåter en korrekt pseudo-marginalisering utav mål-distributionen. Algoritmen kommer sedan att testas på en enkel Gaussisk-modell, en Gaussisk mixturmodell (GMM) och till sist en GMM vars dimension är okänd. Kodningen i detta projekt har utförts i Matlab.
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Murphy, James Kevin. "Hidden states, hidden structures : Bayesian learning in time series models." Thesis, University of Cambridge, 2014. https://www.repository.cam.ac.uk/handle/1810/250355.
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This thesis presents methods for the inference of system state and the learning of model structure for a number of hidden-state time series models, within a Bayesian probabilistic framework. Motivating examples are taken from application areas including finance, physical object tracking and audio restoration. The work in this thesis can be broadly divided into three themes: system and parameter estimation in linear jump-diffusion systems, non-parametric model (system) estimation and batch audio restoration. For linear jump-diffusion systems, efficient state estimation methods based on the variable rate particle filter are presented for the general linear case (chapter 3) and a new method of parameter estimation based on Particle MCMC methods is introduced and tested against an alternative method using reversible-jump MCMC (chapter 4). Non-parametric model estimation is examined in two settings: the estimation of non-parametric environment models in a SLAM-style problem, and the estimation of the network structure and forms of linkage between multiple objects. In the former case, a non-parametric Gaussian process prior model is used to learn a potential field model of the environment in which a target moves. Efficient solution methods based on Rao-Blackwellized particle filters are given (chapter 5). In the latter case, a new way of learning non-linear inter-object relationships in multi-object systems is developed, allowing complicated inter-object dynamics to be learnt and causality between objects to be inferred. Again based on Gaussian process prior assumptions, the method allows the identification of a wide range of relationships between objects with minimal assumptions and admits efficient solution, albeit in batch form at present (chapter 6). Finally, the thesis presents some new results in the restoration of audio signals, in particular the removal of impulse noise (pops and clicks) from audio recordings (chapter 7).
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Choy, Ko-leung Tyrone, and 蔡高亮. "An investigation on the learning of statistics with MINITAB." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1998. http://hub.hku.hk/bib/B31960078.
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Huang, Xin. "A study on the application of machine learning algorithms in stochastic optimal control." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252541.
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By observing a similarity between the goal of stochastic optimal control to minimize an expected cost functional and the aim of machine learning to minimize an expected loss function, a method of applying machine learning algorithm to approximate the optimal control function is established and implemented via neural approximation. Based on a discretization framework, a recursive formula for the gradient of the approximated cost functional on the parameters of neural network is derived. For a well-known Linear-Quadratic-Gaussian control problem, the approximated neural network function obtained with stochastic gradient descent algorithm manages to reproduce to shape of the theoretical optimal control function, and application of different types of machine learning optimization algorithm gives quite close accuracy rate in terms of their associated empirical value function. Furthermore, it is shown that the accuracy and stability of machine learning approximation can be improved by increasing the size of minibatch and applying a finer discretization scheme. These results suggest the effectiveness and appropriateness of applying machine learning algorithm for stochastic optimal control.Genom att observera en likhet mellan målet för stokastisk optimal styrning för att minimera en förväntad kostnadsfunktionell och syftet med maskininlärning att minimera en förväntad förlustfunktion etableras och implementeras en metod för att applicera maskininlärningsalgoritmen för att approximera den optimala kontrollfunktionen via neuralt approximation. Baserat på en diskretiseringsram, härleds en rekursiv formel för gradienten av den approximerade kostnadsfunktionen på parametrarna för neuralt nätverk. För ett välkänt linjärt-kvadratisk-gaussiskt kontrollproblem lyckas den approximerade neurala nätverksfunktionen erhållen med stokastisk gradient nedstigningsalgoritm att reproducera till formen av den teoretiska optimala styrfunktionen och tillämpning av olika typer av algoritmer för maskininlärning optimering ger en ganska nära noggrannhet med avseende på deras motsvarande empiriska värdefunktion. Vidare är det visat att noggrannheten och stabiliteten hos maskininlärning simetrationen kan förbättras genom att öka storleken på minibatch och tillämpa ett finare diskretiseringsschema. Dessa resultat tyder på effektiviteten och lämpligheten av att tillämpa maskininlärningsalgoritmen för stokastisk optimal styrning.
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Shipitsyn, Aleksey. "Statistical Learning with Imbalanced Data." Thesis, Linköpings universitet, Filosofiska fakulteten, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139168.
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In this thesis several sampling methods for Statistical Learning with imbalanced data have been implemented and evaluated with a new metric, imbalanced accuracy. Several modifications and new algorithms have been proposed for intelligent sampling: Border links, Clean Border Undersampling, One-Sided Undersampling Modified, DBSCAN Undersampling, Class Adjusted Jittering, Hierarchical Cluster Based Oversampling, DBSCAN Oversampling, Fitted Distribution Oversampling, Random Linear Combinations Oversampling, Center Repulsion Oversampling. A set of requirements on a satisfactory performance metric for imbalanced learning have been formulated and a new metric for evaluating classification performance has been developed accordingly. The new metric is based on a combination of the worst class accuracy and geometric mean. In the testing framework nonparametric Friedman's test and post hoc Nemenyi’s test have been used to assess the performance of classifiers, sampling algorithms, combinations of classifiers and sampling algorithms on several data sets. A new approach of detecting algorithms with dominating and dominated performance has been proposed with a new way of visualizing the results in a network. From experiments on simulated and several real data sets we conclude that: i) different classifiers are not equally sensitive to sampling algorithms, ii) sampling algorithms have different performance within specific classifiers, iii) oversampling algorithms perform better than undersampling algorithms, iv) Random Oversampling and Random Undersampling outperform many well-known sampling algorithms, v) our proposed algorithms Hierarchical Cluster Based Oversampling, DBSCAN Oversampling with FDO, and Class Adjusted Jittering perform much better than other algorithms, vi) a few good combinations of a classifier and sampling algorithm may boost classification performance, while a few bad combinations may spoil the performance, but the majority of combinations are not significantly different in performance.
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Álvarez, Robles Enrique Josué. "Supervised Learning models with ice hockey data." Thesis, Linköpings universitet, Statistik och maskininlärning, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-167718.
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The technology developments of the last years allow measuring data in almost every field and area nowadays, especially increasing the potential for analytics in branches in which not much analytics have been done due to complicated data access before. The increased number of interest in sports analytics is highly connected to the better technology now available for visual and physical sensors on the one hand and sports as upcoming economic topic holding potentially large revenues and therefore investing interest on the other hand. With the underlying database, precise strategies and individual performance improvements within the field of professional sports are no longer a question of (coach)experience but can be derived from models with statistical accuracy. This thesis aims to evaluate if the available data together with complex and simple supervised machine learning models could generalize from the training data to unseen situations by evaluating performance metrics. Data from games of the ice hockey team of Linköping for the season 2017/2018 is processed with supervised learning algorithms such as binary logistic regression and neural networks. The result of this first step is to determine the strategies of passes by considering both, attempted but failed and successful shots on goals during the game. For that, the original, raw data set was aggregated to game-specific data. After having detected the distinct strategies, they are classified due to their rate of success.
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Choy, Tze Leung. "Sparse distance metric learning." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:a98695a3-0a60-448f-9ec0-63da3c37f7fa.
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A good distance metric can improve the accuracy of a nearest neighbour classifier. Xing et al. (2002) proposed distance metric learning to find a linear transformation of the data so that observations of different classes are better separated. For high-dimensional problems where many un-informative variables are present, it is attractive to select a sparse distance metric, both to increase predictive accuracy but also to aid interpretation of the result. In this thesis, we investigate three different types of sparsity assumption for distance metric learning and show that sparse recovery is possible under each type of sparsity assumption with an appropriate choice of L1-type penalty. We show that a lasso penalty promotes learning a transformation matrix having lots of zero entries, a group lasso penalty recovers a transformation matrix having zero rows/columns and a trace norm penalty allows us to learn a low rank transformation matrix. The regularization allows us to consider a large number of covariates and we apply the technique to an expanded set of basis called rule ensemble to allow for a more flexible fit. Finally, we illustrate an application of the metric learning problem via a document retrieval example and discuss how similarity-based information can be applied to learn a classifier.
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Brodin, Kristoffer. "Statistical Machine Learning from Classification Perspective: : Prediction of Household Ties for Economical Decision Making." Thesis, KTH, Matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-215923.
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In modern society, many companies have large data records over their individual customers, containing information about attributes, such as name, gender, marital status, address, etc. These attributes can be used to link costumers together, depending on whether they share some sort of relationship with each other or not. In this thesis the goal is to investigate and compare methods to predict relationships between individuals in the terms of what we define as a household relationship, i.e. we wish to identify which individuals are sharing living expenses with one another. The objective is to explore the ability of three supervised statistical machine learning methods, namely, logistic regression (LR), artificial neural networks (ANN) and the support vector machine (SVM), to predict these household relationships and evaluate their predictive performance for different settings on their corresponding tuning parameters. Data over a limited population of individuals, containing information about household affiliation and attributes, were available for this task. In order to apply these methods, the problem had to be formulated on a form enabling supervised learning, i.e. a target Y and input predictors X = (X1, …, Xp), based on the set of p attributes associated with each individual, had to be derived. We have presented a technique which forms pairs of individuals under the hypothesis H0, that they share a household relationship, and then a test of significance is constructed. This technique transforms the problem into a standard binary classification problem. A sample of observations could be generated by randomly pair individuals and using the available data over each individual to code the corresponding outcome on Y and X for each random pair. For evaluation and tuning of the three supervised learning methods, the sample was split into a training set, a validation set and a test set. We have seen that the prediction error, in term of misclassification rate, is very small for all three methods since the two classes, H0 is true, and H0 is false, are far away from each other and well separable. The data have shown pronounced linear separability, generally resulting in minor differences in misclassification rate as the tuning parameters are modified. However, some variations in the prediction results due to tuning have been observed, and if also considering computational time and requirements on computational power, optimal settings on the tuning parameters could be determined for each method. Comparing LR, ANN and SVM, using optimal tuning settings, the results from testing have shown that there is no significant difference between the three methods performances and they all predict well. Nevertheless, due to difference in complexity between the methods, we have concluded that SVM is the least suitable method to use, whereas LR most suitable. However, the ANN handles complex and non-linear data better than LR, therefore, for future application of the model, where data might not have such a pronounced linear separability, we find it suitable to consider ANN as well. This thesis has been written at Svenska Handelsbanken, one of the large major banks in Sweden, with offices all around the world. Their headquarters are situated in Kungsträdgården, Stockholm. Computations have been performed using SAS software and data have been processed in SQL relational database management system.I det moderna samhället har många företag stora datasamlingar över sina enskilda kunder, innehållande information om attribut, så som namn, kön, civilstatus, adress etc. Dessa attribut kan användas för att länka samman kunderna beroende på om de delar någon form av relation till varandra eller ej. I denna avhandling är målet att undersöka och jämföra metoder för att prediktera relationer mellan individer i termer av vad vi definierar som en hushållsrelation, d.v.s. vi vill identifiera vilka individer som delar levnadskostnader med varandra. Målsättningen är att undersöka möjligheten för tre övervakade statistiska maskininlärningsmetoder, nämligen, logistisk regression (LR), artificiella neurala nätverk (ANN) och stödvektormaskinen (SVM), för att prediktera dessa hushållsrelationer och utvärdera deras prediktiva prestanda för olika inställningar på deras motsvarande inställningsparametrar. Data över en begränsad mängd individer, innehållande information om hushållsrelation och attribut, var tillgänglig för denna uppgift. För att tillämpa dessa metoder måste problemet formuleras på en form som möjliggör övervakat lärande, d.v.s. en målvariabel Y och prediktorer X = (X1,…,Xp), baserat på uppsättningen av p attribut associerade med varje individ, måste härledas. Vi har presenterat en teknik som utgörs av att skapa par av individer under hypotesen H0, att de delar ett hushållsförhållande, och sedan konstrueras ett signifikanstest. Denna teknik omvandlar problemet till ett standard binärt klassificeringsproblem. Ett stickprov av observationer, för att träna metoderna, kunde genereras av att slumpmässigt para individer och använda informationen från datasamlingarna för att koda motsvarande utfall på Y och X för varje slumpmässigt par. För utvärdering och avstämning av de tre övervakade inlärningsmetoderna delades observationerna i stickprovet in i en träningsmängd, en valideringsmängd och en testmängd. Vi har sett att prediktionsfelet, i form av felklassificeringsfrekvens, är mycket litet för alla metoder och de två klasserna, H0 är sann, och H0 är falsk, ligger långt ifrån varandra och väl separabla. Data har visat sig ha en uttalad linjär separabilitet, vilket generellt resulterar i mycket små skillnader i felklassificeringsfrekvens då inställningsparametrarna modifieras. Dock har vissa variationer i prediktiv prestanda p.g.a. inställningskonfiguration ändå observerats, och om hänsyn även tages till beräkningstid och beräkningskraft, har optimala inställningsparametrar ändå kunnat fastställas för respektive metod. Jämförs därefter LR, ANN och SVM, med optimala parameterinställningar, visar resultaten från testningen att det inte finns någon signifikant skillnad mellan metodernas prestanda och de predikterar alla väl. På grund av skillnad i komplexitet mellan metoderna, har det dock konstaterats att SVM är den minst lämpliga metoden att använda medan LR är lämpligast. ANN hanterar dock komplex och icke-linjära data bättre än LR, därför, för framtida tillämpning av modellen, där data kanske inte uppvisar lika linjär separabilitet, tycker vi att det är lämpligt att även överväga ANN. Denna uppsats har skrivits på Svenska Handelsbanken, en av storbankerna i Sverige, med kontor över hela världen. Huvudkontoret är beläget i Kungsträdgården, Stockholm. Beräkningar har utförts i programvaran SAS och datahantering i databashanteraren SQL.
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Naim, Mohamed M. "Learning curve models for predicting performance of industrial systems." Thesis, Cardiff University, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.363034.
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Andersson, Carl. "Deep learning applied to system identification : A probabilistic approach." Licentiate thesis, Uppsala universitet, Avdelningen för systemteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-397563.
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Machine learning has been applied to sequential data for a long time in the field of system identification. As deep learning grew under the late 00's machine learning was again applied to sequential data but from a new angle, not utilizing much of the knowledge from system identification. Likewise, the field of system identification has yet to adopt many of the recent advancements in deep learning. This thesis is a response to that. It introduces the field of deep learning in a probabilistic machine learning setting for problems known from system identification. Our goal for sequential modeling within the scope of this thesis is to obtain a model with good predictive and/or generative capabilities. The motivation behind this is that such a model can then be used in other areas, such as control or reinforcement learning. The model could also be used as a stepping stone for machine learning problems or for pure recreational purposes. Paper I and Paper II focus on how to apply deep learning to common system identification problems. Paper I introduces a novel way of regularizing the impulse response estimator for a system. In contrast to previous methods using Gaussian processes for this regularization we propose to parameterize the regularization with a neural network and train this using a large dataset. Paper II introduces deep learning and many of its core concepts for a system identification audience. In the paper we also evaluate several contemporary deep learning models on standard system identification benchmarks. Paper III is the odd fish in the collection in that it focuses on the mathematical formulation and evaluation of calibration in classification especially for deep neural network. The paper proposes a new formalized notation for calibration and some novel ideas for evaluation of calibration. It also provides some experimental results on calibration evaluation.
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Vlnas, Pavel. "Management výuky statistických předmětů v kombinovaném studiu." Master's thesis, Vysoká škola ekonomická v Praze, 2009. http://www.nusl.cz/ntk/nusl-19175.
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Currently, there is an urgent need to conduct analysis of teaching statistical courses at the Faculty of Management University of Economics in Prague in order to analyze the current teaching methods, which are at the time of this thesis in the current academic year 2009-2010. Another aim is to propose a new method of teaching that reflect emerging trends in education, which would help students to understand and absorb the learning.
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Peccarelli, Adric M. "A Comparison of Variance and Renyi's Entropy with Application to Machine Learning." Thesis, Northern Illinois University, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10603911.
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This research explores parametric and nonparametric similarities and disagreements between variance and the information theoretic measure of entropy, specifically Renyi’s entropy. A history and known relationships of the two different uncertainty measures is examined. Then, twenty discrete and continuous parametric families are tabulated with their respective variance and Renyi entropy functions ordered to understand the behavior of these two measures of uncertainty. Finally, an algorithm for variable selection using Renyi’s Quadratic Entropy and its kernel estimation is explored and compared to other popular selection methods using real data.