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Academic literature on the topic 'Outlier analyses'

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Published: 10 March 2023

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Journal articles on the topic "Outlier analyses"

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Bhushan, A., M. H. Sharker, and H. A. Karimi. "INCREMENTAL PRINCIPAL COMPONENT ANALYSIS BASED OUTLIER DETECTION METHODS FOR SPATIOTEMPORAL DATA STREAMS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-4/W2 (July 10, 2015): 67–71. http://dx.doi.org/10.5194/isprsannals-ii-4-w2-67-2015.

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In this paper, we address outliers in spatiotemporal data streams obtained from sensors placed across geographically distributed locations. Outliers may appear in such sensor data due to various reasons such as instrumental error and environmental change. Real-time detection of these outliers is essential to prevent propagation of errors in subsequent analyses and results. Incremental Principal Component Analysis (IPCA) is one possible approach for detecting outliers in such type of spatiotemporal data streams. IPCA has been widely used in many real-time applications such as credit card fraud detection, pattern recognition, and image analysis. However, the suitability of applying IPCA for outlier detection in spatiotemporal data streams is unknown and needs to be investigated. To fill this research gap, this paper contributes by presenting two new IPCA-based outlier detection methods and performing a comparative analysis with the existing IPCA-based outlier detection methods to assess their suitability for spatiotemporal sensor data streams.
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Singal, J., G. Silverman, E. Jones, T. Do, B. Boscoe, and Y. Wan. "Machine Learning Classification to Identify Catastrophic Outlier Photometric Redshift Estimates." Astrophysical Journal 928, no. 1 (March 1, 2022): 6. http://dx.doi.org/10.3847/1538-4357/ac53b5.

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Abstract We present results of using a basic binary classification neural network model to identify likely catastrophic outlier photometric redshift estimates of individual galaxies, based only on the galaxies’ measured photometric band magnitude values. We find that a simple implementation of this classification can identify a significant fraction of galaxies with catastrophic outlier photometric redshift estimates while falsely categorizing only a much smaller fraction of non-outliers. These methods have the potential to reduce the errors introduced into science analyses by catastrophic outlier photometric redshift estimates.
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Svabova, Lucia, and Marek Durica. "Being an outlier: a company non-prosperity sign?" Equilibrium 14, no. 2 (June 30, 2019): 359–75. http://dx.doi.org/10.24136/eq.2019.017.

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Research background: The state of financial distress or imminent bankruptcy are very difficult situations that the management of every company wants to avoid. For these reasons, prediction of company bankruptcy or financial distress has been recently in a focus of economists and scientists in many countries over the world. Purpose of the article: Various financial indicators, mostly financial ratios, are usually used to predict the financial distress. In order to create a strong prediction model and a statistically significant prediction of bankruptcy, it is advisable to use a deep statistical analysis of the data. In this paper, we analysed the real financial ratios of Slovak companies from the year 2017. In the phase of data preparation for further analysis, we checked the existence of outliers and found that there are some companies that are multivariate outliers because are significantly different from other companies in the database. Thus, we deeply focused on these outlying companies and analysed whether to be an outlier is a sign of financial distress. Methods: We analysed whether there are much more non-prosperous companies in the set of outlier companies and if their financial indicators are significantly different from those of the prosperous companies. For these analyses, we used testing of the statistical hypotheses, such as the test for equality of means and chi-square test. Findings & Value added: The ratio of non-prosperous companies between the outliers is significantly higher than 50 % and the attributes of non-prosperity and being an outlier are dependent. The means of almost all financial ratios of prosperous and non-prosperous companies among outliers are significantly different.
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Wu, Zifeng, Zhouxiang Wu, and Laurence R. Rilett. "Innovative Nonparametric Method for Data Outlier Filtering." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 10 (September 18, 2020): 167–76. http://dx.doi.org/10.1177/0361198120945697.

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Outlier filtering of empirical travel time data is essential for traffic analyses. Most of the widely applied outlier filtering algorithms are parametric in nature and based on assumed data distributions. The assumption, however, might not hold under unstable traffic conditions. This paper proposes a nonparametric outlier filtering method based on a robust locally weighted regression scatterplot smoothing model. The proposed method identifies outliers based on a data point’s standard residual in the robust local regression model. This approach fits a regression surface with no constraint on parametric distributions and limited influence from outliers. The proposed outlier filtering algorithm can be applied to various data collection technologies and for real-time applications. The performance of the new outlier filtering algorithm is compared with the moving standard deviation method and other traditional filtering algorithms. The test sites include GPS data of an Interstate highway in Indiana and Bluetooth data of an urban arterial roadway in Texas. It is shown that the proposed filtering algorithm has several advantages over the traditional filtering algorithms.
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Bae, Inhyeok, and Un Ji. "Outlier Detection and Smoothing Process for Water Level Data Measured by Ultrasonic Sensor in Stream Flows." Water 11, no. 5 (May 7, 2019): 951. http://dx.doi.org/10.3390/w11050951.

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Water level data sets acquired by ultrasonic sensors in stream-scale channels exhibit relatively large numbers of outliers that are off the measurement range between the ultrasonic sensor and water surface, as well as data dispersion of approximately 2 cm due to random errors such as water waves. Therefore, this study develops a data processing algorithm for outlier removal and smoothing for water level data measured by ultrasonic sensors to consider these characteristics. The outlier removal process includes an initial cutoff process to remove outliers out of the measurement range and an outlier detection process using modified Z-scores based on the median absolute deviation (MAD) of a robust estimator. In addition, an exponentially weighted moving average (EWMA) method is applied to smooth the processed data. Sensitivity analyses are performed for factors that are subjectively set by the user, including the window size for the MAD outlier detection stage, the rejection criterion for the modified Z-score outlier removal stage, and the smoothing constant for the EWMA smoothing stage, based on four different water level data sets acquired by ultrasonic sensors in stream-scale experiments.
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Cobb, Natalie L., Sigrid Collier, Engi F. Attia, Orvalho Augusto, T. Eoin West, and Bradley H. Wagenaar. "Global influenza surveillance systems to detect the spread of influenza-negative influenza-like illness during the COVID-19 pandemic: Time series outlier analyses from 2015–2020." PLOS Medicine 19, no. 7 (July 19, 2022): e1004035. http://dx.doi.org/10.1371/journal.pmed.1004035.

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Background Surveillance systems are important in detecting changes in disease patterns and can act as early warning systems for emerging disease outbreaks. We hypothesized that analysis of data from existing global influenza surveillance networks early in the COVID-19 pandemic could identify outliers in influenza-negative influenza-like illness (ILI). We used data-driven methods to detect outliers in ILI that preceded the first reported peaks of COVID-19. Methods and findings We used data from the World Health Organization’s Global Influenza Surveillance and Response System to evaluate time series outliers in influenza-negative ILI. Using automated autoregressive integrated moving average (ARIMA) time series outlier detection models and baseline influenza-negative ILI training data from 2015–2019, we analyzed 8,792 country-weeks across 28 countries to identify the first week in 2020 with a positive outlier in influenza-negative ILI. We present the difference in weeks between identified outliers and the first reported COVID-19 peaks in these 28 countries with high levels of data completeness for influenza surveillance data and the highest number of reported COVID-19 cases globally in 2020. To account for missing data, we also performed a sensitivity analysis using linear interpolation for missing observations of influenza-negative ILI. In 16 of the 28 countries (57%) included in this study, we identified positive outliers in cases of influenza-negative ILI that predated the first reported COVID-19 peak in each country; the average lag between the first positive ILI outlier and the reported COVID-19 peak was 13.3 weeks (standard deviation 6.8). In our primary analysis, the earliest outliers occurred during the week of January 13, 2020, in Peru, the Philippines, Poland, and Spain. Using linear interpolation for missing data, the earliest outliers were detected during the weeks beginning December 30, 2019, and January 20, 2020, in Poland and Peru, respectively. This contrasts with the reported COVID-19 peaks, which occurred on April 6 in Poland and June 1 in Peru. In many low- and middle-income countries in particular, the lag between detected outliers and COVID-19 peaks exceeded 12 weeks. These outliers may represent undetected spread of SARS-CoV-2, although a limitation of this study is that we could not evaluate SARS-CoV-2 positivity. Conclusions Using an automated system of influenza-negative ILI outlier monitoring may have informed countries of the spread of COVID-19 more than 13 weeks before the first reported COVID-19 peaks. This proof-of-concept paper suggests that a system of influenza-negative ILI outlier monitoring could have informed national and global responses to SARS-CoV-2 during the rapid spread of this novel pathogen in early 2020.
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Reddy, Y. Harshavardhan, M. Hari Srinivas, Adnan Ali, and A. Zaheer Sha. "A Review on Outliers in IoT." South Asian Research Journal of Engineering and Technology 4, no. 6 (November 11, 2022): 134–41. http://dx.doi.org/10.36346/sarjet.2022.v04i06.001.

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In recent decades, the Internet of Things (IoT) has grown rapidly, attracting the attention of scientists and businesspeople. In extreme conditions, autonomously scattered sensor nodes pose a high risk of failure and intrusion into the IoT, skewing sensor values. Abnormal data, anomalies, or outliers are sensor values that depart from norms. When abnormalities are factored into data analytics, the ultimate judgment is affected. Using data-driven algorithms for IoT outlier detection is a cutting-edge tactic in Machine Learning (ML). However, evaluating the effectiveness of implemented ML techniques for outlier detection in IoT, which have the minimal processing power and power sources to ensure data quality, raises several difficulties that have just recently begun to be addressed in the academic literature. This paper analyses the cutting-edge architecture, type, degree, technique, and detection mode of AI and statistical outlier detection strategies in IoTs. Also, each of the ways to find outliers is talked about in detail, along with ways to make them better.
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Höhne, Jan Karem, and Stephan Schlosser. "Investigating the Adequacy of Response Time Outlier Definitions in Computer-Based Web Surveys Using Paradata SurveyFocus." Social Science Computer Review 36, no. 3 (June 1, 2017): 369–78. http://dx.doi.org/10.1177/0894439317710450.

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Web surveys are commonly used in social research because they are usually cheaper, faster, and simpler to conduct than other modes. They also enable researchers to capture paradata such as response times. Particularly, the determination of proper values to define outliers in response time analyses has proven to be an intricate challenge. In fact, to a certain degree, researchers determine them arbitrarily. In this study, we use “SurveyFocus (SF)”—a paradata tool that records the activity of the web-survey pages—to assess outlier definitions based on response time distributions. Our analyses reveal that these common procedures provide relatively sufficient results. However, they are unable to detect all respondents who temporarily leave the survey, causing bias in the response times. Therefore, we recommend a two-step procedure consisting of the utilization of SF and a common outlier definition to attain a more appropriate analysis and interpretation of response times.
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Mao, Jialin, Frederic Scott Resnic, Leonard N. Girardi, Mario Fl Gaudino, and Art Sedrakyan. "Challenges in outlier surgeon assessment in the era of public reporting." Heart 105, no. 9 (November 10, 2018): 721–27. http://dx.doi.org/10.1136/heartjnl-2018-313650.

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ObjectiveTo assess the effect of various evaluation and reporting strategies in determining outlier surgeons, defined by having worse-than-expected mortality after cardiac surgery.MethodsOur study included 33 394 isolated coronary artery bypass graft (CABG) procedures performed by 136 surgeons and 12 172 surgical aortic valve replacement (SAVR) procedures performed by 113 surgeons between 2010 and 2014. Three current methodologies based on the framework of comparing observed and expected (O/E ratio) mortality, with different distributional assumptions, were examined. We further assessed the consistency of outliers detected by these three methods and the impact of using different time windows and aggregating data of CABG and SAVR procedures.ResultsThe three methods were consistent and detected same outliers, with the least conservative method detecting additional outliers (outliers detected for methods 1, 2 and 3: CABG 3 (2.2%), 2 (1.5%) and 8 (5.9%); SAVR 1 (0.9%), 0 (0.0%) and 11 (9.7%)). When numbers of cases recorded were low and events were rare, the two more conservative methods were unlikely to detect outliers unless the O/E ratios were extremely high. However, these two methods were more consistent in detecting the same surgeons as outliers across different time windows for assessment. Of the surgeons who performed both CABG and SAVR, none was an outlier for both procedures when assessed separately. Aggregating data from CABG and SAVR may lead to results to be dominated by the procedure that had a higher caseload.ConclusionsThe choices of outlier assessment method, time window for assessment and data aggregation have an intertwined impact on detecting outlier surgeons, often representing different value assumptions toward patient protection and provider penalty. It is desirable to use different methods as sensitivity analyses, avoid aggregating procedures and avoid rare-event endpoints if possible.
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Beasley, Charles M., Brenda Crowe, Mary Nilsson, LieLing Wu, Rebeka Tabbey, Ryan T. Hietpas, Robert Dean, and Paul S. Horn. "Reference Limits for Outlier Analyses in Randomized Clinical Trials." Therapeutic Innovation & Regulatory Science 51, no. 6 (November 2017): 683–737. http://dx.doi.org/10.1177/2168479017700679.

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Dissertations / Theses on the topic "Outlier analyses"

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Zhang, Ji. "Towards outlier detection for high-dimensional data streams using projected outlier analysis strategy." University of Southern Queensland, Faculty of Sciences, 2008. http://eprints.usq.edu.au/archive/00005645/.

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[Abstract]: Outlier detection is an important research problem in data mining that aims to discover useful abnormal and irregular patterns hidden in large data sets. Most existing outlier detection methods only deal with static data with relatively low dimensionality.Recently, outlier detection for high-dimensional stream data became a new emerging research problem. A key observation that motivates this research is that outliersin high-dimensional data are projected outliers, i.e., they are embedded in lower-dimensional subspaces. Detecting projected outliers from high-dimensional streamdata is a very challenging task for several reasons. First, detecting projected outliers is difficult even for high-dimensional static data. The exhaustive search for the out-lying subspaces where projected outliers are embedded is a NP problem. Second, the algorithms for handling data streams are constrained to take only one pass to process the streaming data with the conditions of space limitation and time criticality. The currently existing methods for outlier detection are found to be ineffective for detecting projected outliers in high-dimensional data streams.In this thesis, we present a new technique, called the Stream Project Outlier deTector (SPOT), which attempts to detect projected outliers in high-dimensionaldata streams. SPOT employs an innovative window-based time model in capturing dynamic statistics from stream data, and a novel data structure containing a set oftop sparse subspaces to detect projected outliers effectively. SPOT also employs a multi-objective genetic algorithm as an effective search method for finding theoutlying subspaces where most projected outliers are embedded. The experimental results demonstrate that SPOT is efficient and effective in detecting projected outliersfor high-dimensional data streams. The main contribution of this thesis is that it provides a backbone in tackling the challenging problem of outlier detection for high-dimensional data streams. SPOT can facilitate the discovery of useful abnormal patterns and can be potentially applied to a variety of high demand applications, such as for sensor network data monitoring, online transaction protection, etc.
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Cheng, Gongxian. "Outlier management in intelligent data analysis." Thesis, Birkbeck (University of London), 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.417120.

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Abghari, Shahrooz. "Data Modeling for Outlier Detection." Licentiate thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-16580.

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This thesis explores the data modeling for outlier detection techniques in three different application domains: maritime surveillance, district heating, and online media and sequence datasets. The proposed models are evaluated and validated under different experimental scenarios, taking into account specific characteristics and setups of the different domains. Outlier detection has been studied and applied in many domains. Outliers arise due to different reasons such as fraudulent activities, structural defects, health problems, and mechanical issues. The detection of outliers is a challenging task that can reveal system faults, fraud, and save people's lives. Outlier detection techniques are often domain-specific. The main challenge in outlier detection relates to modeling the normal behavior in order to identify abnormalities. The choice of model is important, i.e., an incorrect choice of data model can lead to poor results. This requires a good understanding and interpretation of the data, the constraints, and the requirements of the problem domain. Outlier detection is largely an unsupervised problem due to unavailability of labeled data and the fact that labeled data is expensive. We have studied and applied a combination of both machine learning and data mining techniques to build data-driven and domain-oriented outlier detection models. We have shown the importance of data preprocessing as well as feature selection in building suitable methods for data modeling. We have taken advantage of both supervised and unsupervised techniques to create hybrid methods. For example, we have proposed a rule-based outlier detection system based on open data for the maritime surveillance domain. Furthermore, we have combined cluster analysis and regression to identify manual changes in the heating systems at the building level. Sequential pattern mining for identifying contextual and collective outliers in online media data have also been exploited. In addition, we have proposed a minimum spanning tree clustering technique for detection of groups of outliers in online media and sequence data. The proposed models have been shown to be capable of explaining the underlying properties of the detected outliers. This can facilitate domain experts in narrowing down the scope of analysis and understanding the reasons of such anomalous behaviors. We have also investigated the reproducibility of the proposed models in similar application domains.
Scalable resource-efficient systems for big data analytics
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Birch, Gary Edward. "Single trial EEG signal analysis using outlier information." Thesis, University of British Columbia, 1988. http://hdl.handle.net/2429/28626.

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The goal of this thesis work was to study the characteristics of the EEG signal and then, based on the insights gained from these studies, pursue an initial investigation into a processing method that would extract useful event related information from single trial EEG. The fundamental tool used to study the EEG signal characteristics was autoregressive modeling. Early investigations pointed to the need to employ robust techniques in both model parameter estimation and signal estimation applications. Pursuing robust techniques ultimately led to the development of a single trial processing method which was based on a simple neurological model that assumed an additive outlier nature of event related potentials to the ongoing EEG process. When event related potentials, such as motor related potentials, are generated by a unique additional process they are "added" into the ongoing process and hence, will appear as additive outlier content when considered from the point of view of the ongoing process. By modeling the EEG with AR models with robustly estimated (GM-estimates) parameters and by using those models in a robust signal estimator, a "cleaned" EEG signal is obtained. The outlier content, data that is extracted from the EEG during cleaning, is then processed to yield event related information. The EEG from four subjects formed the basis of the initial investigation into the viability of this single trial processing scheme. The EEG was collected under two conditions: an active task in which subjects performed a skilled thumb movement and an idle task in which subjects remained alert but did not carry out any motor activity. The outlier content was processed which provided single trial outlier waveforms. In the active case these waveforms possessed consistent features which were found to be related to events in the individual thumb movements. In the idle case the waveforms did not contain consistent features. Bayesian classification of active trials versus idle trials was carried out using a cost statistic resulting from the application of dynamic time warping to the outlier waveforms. Across the four subjects, when the decision boundary was set with the cost of misclassification equal, 93% of the active trials were classified correctly and 18% of the idle trials were incorrectly classified as active. When the cost of misclassifying an idle trial was set to be five times greater, 80% of the active trials were classified correctly and only 1.7% of the idle trials were incorrectly classified as active.
Applied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate
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Mitchell, Napoleon. "Outliers and Regression Models." Thesis, University of North Texas, 1992. https://digital.library.unt.edu/ark:/67531/metadc279029/.

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The mitigation of outliers serves to increase the strength of a relationship between variables. This study defined outliers in three different ways and used five regression procedures to describe the effects of outliers on 50 data sets. This study also examined the relationship among the shape of the distribution, skewness, and outliers.
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Soon, Shih Chung. "On detection of extreme data points in cluster analysis." Connect to resource, 1987. http://rave.ohiolink.edu/etdc/view.cgi?acc%5Fnum=osu1262886219.

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Robson, Geoffrey. "Multiple outlier detection and cluster analysis of multivariate normal data." Thesis, Stellenbosch : Stellenbosch University, 2003. http://hdl.handle.net/10019.1/53508.

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Thesis (MscEng)--Stellenbosch University, 2003.
ENGLISH ABSTRACT: Outliers may be defined as observations that are sufficiently aberrant to arouse the suspicion of the analyst as to their origin. They could be the result of human error, in which case they should be corrected, but they may also be an interesting exception, and this would deserve further investigation. Identification of outliers typically consists of an informal inspection of a plot of the data, but this is unreliable for dimensions greater than two. A formal procedure for detecting outliers allows for consistency when classifying observations. It also enables one to automate the detection of outliers by using computers. The special case of univariate data is treated separately to introduce essential concepts, and also because it may well be of interest in its own right. We then consider techniques used for detecting multiple outliers in a multivariate normal sample, and go on to explain how these may be generalized to include cluster analysis. Multivariate outlier detection is based on the Minimum Covariance Determinant (MCD) subset, and is therefore treated in detail. Exact bivariate algorithms were refined and implemented, and the solutions were used to establish the performance of the commonly used heuristic, Fast–MCD.
AFRIKAANSE OPSOMMING: Uitskieters word gedefinieer as waarnemings wat tot s´o ’n mate afwyk van die verwagte gedrag dat die analis wantrouig is oor die oorsprong daarvan. Hierdie waarnemings mag die resultaat wees van menslike foute, in welke geval dit reggestel moet word. Dit mag egter ook ’n interressante verskynsel wees wat verdere ondersoek benodig. Die identifikasie van uitskieters word tipies informeel deur inspeksie vanaf ’n grafiese voorstelling van die data uitgevoer, maar hierdie benadering is onbetroubaar vir dimensies groter as twee. ’n Formele prosedure vir die bepaling van uitskieters sal meer konsekwente klassifisering van steekproefdata tot gevolg hˆe. Dit gee ook geleentheid vir effektiewe rekenaar implementering van die tegnieke. Aanvanklik word die spesiale geval van eenveranderlike data behandel om noodsaaklike begrippe bekend te stel, maar ook aangesien dit in eie reg ’n area van groot belang is. Verder word tegnieke vir die identifikasie van verskeie uitskieters in meerveranderlike, normaal verspreide data beskou. Daar word ook ondersoek hoe hierdie idees veralgemeen kan word om tros analise in te sluit. Die sogenaamde Minimum Covariance Determinant (MCD) subversameling is fundamenteel vir die identifikasie van meerveranderlike uitskieters, en word daarom in detail ondersoek. Deterministiese tweeveranderlike algoritmes is verfyn en ge¨ımplementeer, en gebruik om die effektiwiteit van die algemeen gebruikte heuristiese algoritme, Fast–MCD, te ondersoek.
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Halldestam, Markus. "ANOVA - The Effect of Outliers." Thesis, Uppsala universitet, Statistiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-295864.

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This bachelor’s thesis focuses on the effect of outliers on the one-way analysis of variance and examines whether the estimate in ANOVA is robust and whether the actual test itself is robust from influence of extreme outliers. The robustness of the estimates is examined using the breakdown point while the robustness of the test is examined by simulating the hypothesis test under some extreme situations. This study finds evidence that the estimates in ANOVA are sensitive to outliers, i.e. that the procedure is not robust. Samples with a larger portion of extreme outliers have a higher type-I error probability than the expected level.
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Astl, Stefan Ludwig. "Suboptimal LULU-estimators in measurements containing outliers." Thesis, Stellenbosch : Stellenbosch University, 2013. http://hdl.handle.net/10019.1/85833.

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Thesis (MSc)--Stellenbosch University, 2013.
ENGLISH ABSTRACT: Techniques for estimating a signal in the presence of noise which contains outliers are currently not well developed. In this thesis, we consider a constant signal superimposed by a family of noise distributions structured as a tunable mixture f(x) = α g(x) + (1 − α) h(x) between finitesupport components of “well-behaved” noise with small variance g(x) and of “impulsive” noise h(x) with a large amplitude and strongly asymmetric character. When α ≈ 1, h(x) can for example model a cosmic ray striking an experimental detector. In the first part of our work, a method for obtaining the expected values of the positive and negative pulses in the first resolution level of a LULU Discrete Pulse Transform (DPT) is established. Subsequent analysis of sequences smoothed by the operators L1U1 or U1L1 of LULU-theory shows that a robust estimator for the location parameter for g is achieved in the sense that the contribution by h to the expected average of the smoothed sequences is suppressed to order (1 − α)2 or higher. In cases where the specific shape of h can be difficult to guess due to the assumed lack of data, it is thus also shown to be of lesser importance. Furthermore, upon smoothing a sequence with L1U1 or U1L1, estimators for the scale parameters of the model distribution become easily available. In the second part of our work, the same problem and data is approached from a Bayesian inference perspective. The Bayesian estimators are found to be optimal in the sense that they make full use of available information in the data. Heuristic comparison shows, however, that Bayes estimators do not always outperform the LULU estimators. Although the Bayesian perspective provides much insight into the logical connections inherent in the problem, its estimators can be difficult to obtain in analytic form and are slow to compute numerically. Suboptimal LULU-estimators are shown to be reasonable practical compromises in practical problems.
AFRIKAANSE OPSOMMING: Tegnieke om ’n sein af te skat in die teenwoordigheid van geraas wat uitskieters bevat is tans nie goed ontwikkel nie. In hierdie tesis aanskou ons ’n konstante sein gesuperponeer met ’n familie van geraasverdelings wat as verstelbare mengsel f(x) = α g(x) + (1 − α) h(x) tussen eindige-uitkomsruimte geraaskomponente g(x) wat “goeie gedrag” en klein variansie toon, plus “impulsiewe” geraas h(x) met groot amplitude en sterk asimmetriese karakter. Wanneer α ≈ 1 kan h(x) byvoorbeeld ’n kosmiese straal wat ’n eksperimentele apparaat tref modelleer. In die eerste gedeelte van ons werk word ’n metode om die verwagtingswaardes van die positiewe en negatiewe pulse in die eerste resolusievlak van ’n LULU Diskrete Pulse Transform (DPT) vasgestel. Die analise van rye verkry deur die inwerking van die gladstrykers L1U1 en U1L1 van die LULU-teorie toon dat hul verwagte gemiddelde waardes as afskatters van die liggingsparameter van g kan dien wat robuus is in die sin dat die bydrae van h tot die gemiddeld van orde grootte (1 − α)2 of hoër is. Die spesifieke vorm van h word dan ook onbelangrik. Daar word verder gewys dat afskatters vir die relevante skaalparameters van die model maklik verkry kan word na gladstryking met die operatore L1U1 of U1L1. In die tweede gedeelte van ons werk word dieselfde probleem en data vanuit ’n Bayesiese inferensie perspektief benader. Die Bayesiese afskatters word as optimaal bevind in die sin dat hulle vol gebruikmaak van die beskikbare inligting in die data. Heuristiese vergelyking wys egter dat Bayesiese afskatters nie altyd beter vaar as die LULU afskatters nie. Alhoewel die Bayesiese sienswyse baie insig in die logiese verbindings van die probleem gee, kan die afskatters moeilik wees om analities af te lei en stadig om numeries te bereken. Suboptimale LULU-beramers word voorgestel as redelike praktiese kompromieë in praktiese probleme.
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Lipkovich, Ilya A. "Bayesian Model Averaging and Variable Selection in Multivariate Ecological Models." Diss., Virginia Tech, 2002. http://hdl.handle.net/10919/11045.

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Bayesian Model Averaging (BMA) is a new area in modern applied statistics that provides data analysts with an efficient tool for discovering promising models and obtaining esti-mates of their posterior probabilities via Markov chain Monte Carlo (MCMC). These probabilities can be further used as weights for model averaged predictions and estimates of the parameters of interest. As a result, variance components due to model selection are estimated and accounted for, contrary to the practice of conventional data analysis (such as, for example, stepwise model selection). In addition, variable activation probabilities can be obtained for each variable of interest. This dissertation is aimed at connecting BMA and various ramifications of the multivari-ate technique called Reduced-Rank Regression (RRR). In particular, we are concerned with Canonical Correspondence Analysis (CCA) in ecological applications where the data are represented by a site by species abundance matrix with site-specific covariates. Our goal is to incorporate the multivariate techniques, such as Redundancy Analysis and Ca-nonical Correspondence Analysis into the general machinery of BMA, taking into account such complicating phenomena as outliers and clustering of observations within a single data-analysis strategy. Traditional implementations of model averaging are concerned with selection of variables. We extend the methodology of BMA to selection of subgroups of observations and im-plement several approaches to cluster and outlier analysis in the context of the multivari-ate regression model. The proposed algorithm of cluster analysis can accommodate re-strictions on the resulting partition of observations when some of them form sub-clusters that have to be preserved when larger clusters are formed.
Ph. D.
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Books on the topic "Outlier analyses"

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Aggarwal, Charu C. Outlier Analysis. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-47578-3.

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Aggarwal, Charu C. Outlier Analysis. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6396-2.

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Aggarwal, Charu C. Outlier Analysis. New York, NY: Springer New York, 2013.

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Rousseeuw, Peter J. Robust regression and outlier detection. New York: Wiley, 1987.

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Meier, Alan. An analysis of outliers in the RSDP. Berkeley, Calif: Applied Science Division, Lawrence Berkeley Laboratory, University of California, 1988.

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Marchand, J. P. Distributions: An outline. Mineola, N.Y: Dover Publications, 2007.

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Weissmuller, Johnny J. Automated test outline development: Research findings. Brooks Air Force Base, Tex: Air Force Human Resources Laboratory, Air Force Systems Command, 1989.

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R, Spiegel Murray, and Wrede Robert C, eds. Schaum's outline of advanced calculus. 3rd ed. New York: McGraw-Hill, 2010.

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Schaum's outline of theory and problems of vector analysis. Maidenhead: McGraw-Hill, 1988.

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Toby, Lewis, ed. Outliers in statistical data. 3rd ed. Chichester: Wiley, 1994.

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Book chapters on the topic "Outlier analyses"

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Kontala, Janne, Mika Lassander, and Nurit Novis-Deutsch. "Searching for Uncommon Worldviews: ‘Idiosyncratic’ and ‘Divided’ Outlooks in a Global Sample of Young Adults." In The Diversity Of Worldviews Among Young Adults, 113–31. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94691-3_6.

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AbstractIn this chapter, we explore uncommon worldviews, meaning that we take a closer look at outlier respondents in a larger international sample from 12 countries. These outliers are the ones whose personal outlooks did not match any of the major worldview types found in the national case studies. First, we identify shared patterns amongst these respondents. Second, we place these outlier outlook types on a broader worldview map. Juxtaposing the outlier outlooks with the results from other case studies allows us to identify idiosyncratic worldviews. Certain outlooks would not stand out in analyses of single case studies, but bringing them together in a cross-cultural comparison enables us to see patterns shared by individuals across different national contexts. This also reveals better such worldviews that incorporate those elements, which normally are distributed amongst opposing viewpoints. The emergence of these outlook types can support the development of a nuanced theory of religious subjectivities.
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Aggarwal, Charu C. "An Introduction to Outlier Analysis." In Outlier Analysis, 1–34. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47578-3_1.

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Aggarwal, Charu C. "Outlier Detection in Discrete Sequences." In Outlier Analysis, 311–44. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47578-3_10.

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Aggarwal, Charu C. "Spatial Outlier Detection." In Outlier Analysis, 345–68. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47578-3_11.

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Aggarwal, Charu C. "Outlier Detection in Graphs and Networks." In Outlier Analysis, 369–97. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47578-3_12.

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Aggarwal, Charu C. "Applications of Outlier Analysis." In Outlier Analysis, 399–422. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47578-3_13.

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Aggarwal, Charu C. "Probabilistic and Statistical Models for Outlier Detection." In Outlier Analysis, 35–64. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47578-3_2.

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Aggarwal, Charu C. "Linear Models for Outlier Detection." In Outlier Analysis, 65–110. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47578-3_3.

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Aggarwal, Charu C. "Proximity-Based Outlier Detection." In Outlier Analysis, 111–47. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47578-3_4.

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Aggarwal, Charu C. "High-Dimensional Outlier Detection: The Subspace Method." In Outlier Analysis, 149–84. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47578-3_5.

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Conference papers on the topic "Outlier analyses"

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Kim, Hyun-Ki, and Si-Yeol Shin. "Application of Statistical Geo-Sapatial Information Analyses to Outlier Detection." In 5th Asian-Pacific Symposium on Structural Reliability and its Applications. Singapore: Research Publishing Services, 2012. http://dx.doi.org/10.3850/978-981-07-2219-7_p147.

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Gao, Rui, Li Shen, Kwee-Yan Teh, Penghui Ge, Fengnian Zhao, and David L. S. Hung. "Effects of Outlier Flow Field on the Characteristics of In-Cylinder Coherent Structures Identified by POD-Based Conditional Averaging and Quadruple POD." In ASME 2018 Internal Combustion Engine Division Fall Technical Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/icef2018-9561.

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Proper Orthogonal Decomposition (POD) offers an approach to quantify cycle-to-cycle variation (CCV) of the flow field inside the internal combustion engine cylinder. POD decomposes instantaneous flow fields (also called snapshots) into a series of orthonormal flow patterns (called POD modes) and the corresponding mode coefficients. The POD modes are rank-ordered by decreasing kinetic energy content, and the low-order, high-energy modes are interpreted as constituting the large-scale coherent flow structure that varies from engine cycle to engine cycle. Various POD-based analysis techniques have thus been proposed to characterize engine flow field CCV using these low-order modes. The validity of such POD-based analyses rests, as a matter of course, on the reliability of the underlying POD results (modes and coefficients). Yet a POD mode can be disproportionately skewed by a single outlier snapshot within a large data set, and an algorithm exists to define and identify such outliers. In this paper, the effects of a candidate outlier snapshot on the results of POD-based conditional averaging and quadruple POD analyses are examined for two sets of crank angle-resolved flow fields on the mid-tumble plane of an optical engine cylinder recorded by high-speed particle image velocimetry. The results with and without the candidate outlier are compared and contrasted. In the case of POD-based conditional averaging, the presence of the outlier scrambles the composition of snapshot subsets that define large-scale flow pattern variations, and thus substantially alters the coherent flow structures that are identified; for quadruple POD, the shape of coherent structures as well as the number of modes to define them are not significantly affected by the outlier.
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Flamini, Vittoria, and Boyce E. Griffith. "Optimal Constitutive Parameters and Subject Specific Variability: An Application to the Aortic Sinuses." In ASME 2013 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/sbc2013-14633.

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Advanced analyses of soft biological tissues have shown substantial subject-specific variability in mechanical properties [1]. Such variability is also easily observed at a geometrical or morphological level, and has been reported also in mechanical tests on biological tissue samples [1, 2]. While there is wide interest in reproducing accurate geometries for subject-specific modeling, constitutive parameters for mechanical models often use averaged data from mechanical tests [3]. Outliers are typically neglected, and only the ‘mean’ tissue behavior is considered. However, due to an increased interest in using multi-scale and finite element (FE) models for medical device testing and surgical planning [4], understanding of the variability of the outlier tests becomes increasingly important. In particular, by using detailed mechanistic constitutive models, it might be possible to classify the different mechanical behaviors observed on the basis of the changes in the constitutive parameters. This process could lead to the definition of a library of different ‘healthy’ or ‘diseased’ constitutive parameters to be used in computational analyses.
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Nigam, Nidhi, Tripti Saxena, and Vineet Richhariya. "Global high dimension outlier algorithm for efficient clustering & outlier detection." In 2016 Symposium on Colossal Data Analysis and Networking (CDAN). IEEE, 2016. http://dx.doi.org/10.1109/cdan.2016.7570924.

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Yousri, Noha A., Mohammed A. Ismail, and Mohamed S. Kamel. "Fuzzy outlier analysis a combined clustering - outlier detection approach." In 2007 IEEE International Conference on Systems, Man and Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icsmc.2007.4413873.

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Xu, Honglong, Rui Mao, Hao Liao, Minhua Lu, and He Zhang. "Closest neighbors excluded outlier detection." In 2016 IEEE International Conference of Online Analysis and Computing Science (ICOACS). IEEE, 2016. http://dx.doi.org/10.1109/icoacs.2016.7563058.

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Ruffolo, Alessandro. "Outlier Analysis for SETI." In 54th International Astronautical Congress of the International Astronautical Federation, the International Academy of Astronautics, and the International Institute of Space Law. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2003. http://dx.doi.org/10.2514/6.iac-03-iaa.9.p.03.

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Xiaohui Liu. "Strategies for outlier analysis." In IEE Two-day Colloquium on Knowledge Discovery and Data Mining. IEE, 1998. http://dx.doi.org/10.1049/ic:19980546.

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Dalmia, Ayushi, Manish Gupta, and Vasudeva Varma. "Query-based Graph Cuboid Outlier Detection." In ASONAM '15: Advances in Social Networks Analysis and Mining 2015. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2808797.2810061.

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Shih, H. H., C. Long, M. Bushnell, and K. Hathaway. "Intercomparison of Wave Data Between Triaxys Directional Wave Buoy, ADCP, and Other Reference Wave Instruments." In ASME 2005 24th International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2005. http://dx.doi.org/10.1115/omae2005-67235.

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The use of Triaxys directional wave buoy and acoustic Doppler current profiler (ADCP) for wave measurements are relatively recent. The US National Oceanic and Atmospheric Administration’s (NOAA) National Ocean Service (NOS) acquired these instruments in 2001 and systematic laboratory and field tests were conducted during 2001–2002. This paper describes the field tests conducted near the US Army Corps of Engineers’ Field Research Facility (FRF) ocean pier and near the Barren Islands in the Chesapeake Bay. At the FRF site, Triaxys buoy wave measurements were compared with FRF’s field standards of pressure sensor arrays and Datawell Waverider buoy. For the Bay test, ADCP was compared with the Triaxys buoy. There are significant numbers of outlier in the Triaxys peak periods at both test sites. In the Chesapeake Bay, which is dominated by high frequency and low energy waves, there is much scatter in the Triaxys data for significant wave heights below 0.2 m. Detailed analyses were performed after these outliers and noisy data were removed. Statistics of differences in significant wave heights, peak periods and directions between each comparative pair were computed and characteristics of frequency and frequency-direction spectra were examined. Overall, correlations between each instrument pair are very good in significant wave heights, fair in wave peak periods (except the ADCP/Triaxys pair), and marginal in wave directions. Triaxys buoy compared better with Waverider buoy than with others. Both ADCP and FRF pressure sensor array exhibit higher resolution in detecting multi-modal and multi-frequency waves. In most cases, energy distribution of spectral peaks in Triaxys buoy data differs significantly from those obtained from FRF pressure sensor array and ADCP.
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Reports on the topic "Outlier analyses"

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Peter, J. M., M. G. Gadd, C. Jiang, and J. Reyes. Organic geochemistry and petrology of sedimentary exhalative Pb-Zn and polymetallic hyper-enriched black shale deposits in the Selwyn Basin, Yukon. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/328017.

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Paleozoic strata of the Selwyn Basin host sedimentary exhalative (SEDEX) Pb-Zn deposits, and age-correlative strata of the Richardson trough host polymetallic hyper-enriched black shale (HEBS) deposits. In both deposit types, organic matter is spatially and temporally associated with mineralization. We investigated the characteristics of organic matter in mineralization and unmineralized host rocks in the XY Central SEDEX deposit in the Howard's Pass district, and the Nick and Peel River HEBS deposits in the Richardson trough using Rock-Eval pyrolysis, organic petrography, and solvent extraction and gas chromatography mass spectrometry (GCMS) analysis of the soluble organic matter (SOM). All samples experienced extremely high thermal maturity (Tmax up to 599°C), indicating they contain low SOM. Rock-Eval parameters S1, S2, HI, and OI values are low. Total organic carbon (TOC) values are low for Nick and Peel River and are generally higher for XY Central. Residual carbon values are universally high. Mineral carbon values are low for deposits studied (one outlier). Pyrobitumen reflectance is mostly below 5.80%. Full-scan GCMS analyses of SOM reveal that most, if not all, high molecular weight hydrocarbons, including biomarkers, have been lost due to thermal cracking and many detected peaks are likely due to contaminants introduced during sampling.
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Mathew, Jijo K., Christopher M. Day, Howell Li, and Darcy M. Bullock. Curating Automatic Vehicle Location Data to Compare the Performance of Outlier Filtering Methods. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317435.

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Agencies use a variety of technologies and data providers to obtain travel time information. The best quality data can be obtained from second-by-second tracking of vehicles, but that data presents many challenges in terms of privacy, storage requirements and analysis. More frequently agencies collect or purchase segment travel time based upon some type of matching of vehicles between two spatially distributed points. Typical methods for that data collection involve license plate re-identification, Bluetooth, Wi-Fi, or some type of rolling DSRC identifier. One of the challenges in each of these sampling techniques is to employ filtering techniques to remove outliers associated with trip chaining, but not remove important features in the data associated with incidents or traffic congestion. This paper describes a curated data set that was developed from high-fidelity GPS trajectory data. The curated data contained 31,621 vehicle observations spanning 42 days; 2550 observations had travel times greater than 3 minutes more than normal. From this baseline data set, outliers were determined using GPS waypoints to determine if the vehicle left the route. Two performance measures were identified for evaluating three outlier-filtering algorithms by the proportion of true samples rejected and proportion of outliers correctly identified. The effectiveness of the three methods over 10-minute sampling windows was also evaluated. The curated data set has been archived in a digital repository and is available online for others to test outlier-filtering algorithms.
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Sadler, Brian M., and Stephen D. Casey. On Periodic Pulse Interval Analysis with Outliers and Missing Observations. Fort Belvoir, VA: Defense Technical Information Center, January 1996. http://dx.doi.org/10.21236/ada454910.

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Montgomery, Raymond B., and Athelstan Fred Spilhaus. Examples and outline of certain modifications in isentropic analysis. Woods Hole Oceanographic Institution, December 2022. http://dx.doi.org/10.1575/1912/29557.

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Isentropic analysis in this country originated with a particular purpose in view, namely as a means of using moisture distribution to determine flow patterns in the atmosphere It revealed, very successfully, certain theoretically anticipated patterns. Subsequently it has come into general use in connection with upper-air analysis but for the most part its application is dominated by the original particular purpose. A rather different approach is to use isentropic analysis in a more purely descriptive fashion as the principal tool for upper-air analysis. This demands that an isentropic chart represent synoptically as much useful information as possible and that all phases of its preparation receive due care.
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Taplin, Ross, and Adrian E. Raftery. Analysis of Agricultural Field Trials in the Presence of Outliers and Fertility Jumps. Fort Belvoir, VA: Defense Technical Information Center, September 1991. http://dx.doi.org/10.21236/ada242454.

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Matheu, Enrique E., Robert L. Hall, and Raju V. Kala. Folsom Dam Outlet Works Modification Project: Dynamic Stress Analysis of Overflow and Nonoverflow Sections. Fort Belvoir, VA: Defense Technical Information Center, September 2004. http://dx.doi.org/10.21236/ada427798.

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Michael G. McKellar and Edwin A. Harvego. Analysis of Reference Design for Nuclear-Assisted Hydrogen Production at 750?C Reactor Outlet Temperature. Office of Scientific and Technical Information (OSTI), May 2010. http://dx.doi.org/10.2172/984544.

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Macdonald, Stuart, Kamil Yilmaz, Chamin Herath, J. M. Berger, Suraj Lakhani, Lella Nouri, and Maura Conway. The European Far-Right Online: An Exploratory Twitter Outlink Analysis of German & French Far-Right Online Ecosystems. RESOLVE Network, May 2022. http://dx.doi.org/10.37805/remve2022.2.

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Seeking to explore the nature of European far-right online ecosystems, this research report examines the outlinking activity of identified pro-far-right users among the followers of the official Twitter accounts of two prominent far-right European political parties, Germany’s Alternative für Deutschland (AfD) and France’s Rassemblement National (RN). Employing a three-layered analysis, the report explores not just the top-level domains outlinked to by its sample of AfD and RN Twitter followers but combines this with analysis of the technical specifications of the content types outlinked to and treatment of the socio-political nature of the content arrived at by clicking on the most tweeted URLs. This results in the provision of a more thorough and cohesive view of this online ecosystem than contained in other similar studies.
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Bodnenko, Dmytro M., Halyna A. Kuchakovska, Volodymyr V. Proshkin, and Oksana S. Lytvyn. Using a virtual digital board to organize student’s cooperative learning. [б. в.], November 2020. http://dx.doi.org/10.31812/123456789/4419.

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The article substantiates the importance of using a virtual digital board to organize student’s cooperative learning in the conditions of distance education, incl. social distance (for the quarantine period 2020). The main advantages of using a virtual digital board are outlined and their functions for the organization of cooperative education are compared. An analysis of the benefits of using virtual digital boards and a survey of experts made it possible to identify the most popular virtual digital boards: Wiki-Wall, Glogster, PadLet, Linoit, Twidla, Trello, Realtimeboard (Miro), Rizzoma. The comparison of the functions of virtual digital boards outlines their ability to organize students’ cooperative learning. The structure of the module E-Learning “Creating education content with tools of virtual digital board Padlet” is presented in the system LMS Moodle. The results of the experiment are presented, which show the effectiveness of the use of instruments of the virtual digital board to organize student’s cooperative learning. Perspectives of researches in developing methods of using a virtual digital board by students of natural-mathematical specialties are determined.
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Linke, Ethan, Nazmina Mahmoudzadeh, and Darren Holland. Prioritising Foodborne Disease with Multi-Criteria Decision Analysis. Food Standards Agency, November 2021. http://dx.doi.org/10.46756/sci.fsa.gex408.

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In recent years, the FSA has published a series of research projects which have produced estimates of the frequency and burden of thirteen different foodborne diseases. This document outlines the methodology and results of a multi-criteria decision analysis (MCDA) used to rank them in order of their detrimental effect on UK society.
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