Relevant bibliographies by topics / Statistical inferences

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Statistical inferences'

Author: Grafiati
Published: 25 January 2025

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Journal articles on the topic "Statistical inferences"

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Luo, Yu, and Jiaying Zhao. "Statistical Learning Creates Novel Object Associations via Transitive Relations." Psychological Science 29, no. 8 (May 22, 2018): 1207–20. http://dx.doi.org/10.1177/0956797618762400.

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A remarkable ability of the cognitive system is to make novel inferences on the basis of prior experiences. What mechanism supports such inferences? We propose that statistical learning is a process through which transitive inferences of new associations are made between objects that have never been directly associated. After viewing a continuous sequence containing two base pairs (e.g., A–B, B–C), participants automatically inferred a transitive pair (e.g., A–C) where the two objects had never co-occurred before (Experiment 1). This transitive inference occurred in the absence of explicit awareness of the base pairs. However, participants failed to infer the transitive pair from three base pairs (Experiment 2), showing the limits of the transitive inference (Experiment 3). We further demonstrated that this transitive inference can operate across the categorical hierarchy (Experiments 4–7). The findings revealed a novel consequence of statistical learning in which new transitive associations between objects are implicitly inferred.
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NIELSEN, RASMUS, and MARK A. BEAUMONT. "Statistical inferences in phylogeography." Molecular Ecology 18, no. 6 (March 2009): 1034–47. http://dx.doi.org/10.1111/j.1365-294x.2008.04059.x.

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Philip, G. M., and D. F. Watson. "Probabilism in Geological Data Analysis." Geological Magazine 124, no. 6 (November 1987): 577–83. http://dx.doi.org/10.1017/s0016756800017404.

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AbstractThe way in which probability can enter the interpretation of geological data is outlined. Probabilistic models are introduced in data synopses to allow statistical inferences about samples and population parameters. Inferences of this type are a special application of mathematical theory and have little to do with testing scientific generalizations. The logic of statistical inference, as used in operations research, is contrasted with that of geological inference. Geology is a cumulative science; its methodology is not that of experimentation with formal replication; inferences are developed from observations and tested and refined against new data, often by different investigators at different times and in different places. Because of the way in which inferences are drawn, sampling in geology is purposive. Random sampling, necessary for inferences based on sampling theory, is unattainable in most geological contexts. Different classes of geological measurements require careful consideration as to their appropriate form of synopsis, and, particularly, as to whether a parametric probability model is applicable.
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Zhang, Jin-Ting, and Jianwei Chen. "Statistical inferences for functional data." Annals of Statistics 35, no. 3 (July 2007): 1052–79. http://dx.doi.org/10.1214/009053606000001505.

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Watkins, A. J. "Statistical inferences for breakdown voltages." IEEE Transactions on Dielectrics and Electrical Insulation 7, no. 6 (2000): 869–71. http://dx.doi.org/10.1109/94.892002.

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Turner, Dana P., Hao Deng, and Timothy T. Houle. "Bayesian Approaches to Statistical Inferences." Headache: The Journal of Head and Face Pain 60, no. 9 (September 29, 2020): 1879–85. http://dx.doi.org/10.1111/head.13952.

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Kolokolov, Aleksey, Giulia Livieri, and Davide Pirino. "Statistical inferences for price staleness." Journal of Econometrics 218, no. 1 (September 2020): 32–81. http://dx.doi.org/10.1016/j.jeconom.2020.01.021.

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Raymond, Jean, and Tim E. Darsaut. "Understanding statistical populations and inferences." Neurochirurgie 71, no. 1 (January 2025): 101608. http://dx.doi.org/10.1016/j.neuchi.2024.101608.

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Wang, Yingxu. "Inference Algebra (IA)." International Journal of Cognitive Informatics and Natural Intelligence 6, no. 1 (January 2012): 21–47. http://dx.doi.org/10.4018/jcini.2012010102.

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Inference as the basic mechanism of thought is abilities gifted to human beings, which is a cognitive process that creates rational causations between a pair of cause and effect based on empirical arguments, formal reasoning, and/or statistical norms. It’s recognized that a coherent theory and mathematical means are needed for dealing with formal causal inferences. Presented is a novel denotational mathematical means for formal inferences known as Inference Algebra (IA) and structured as a set of algebraic operators on a set of formal causations. The taxonomy and framework of formal causal inferences of IA are explored in three categories: a) Logical inferences; b) Analytic inferences; and c) Hybrid inferences. IA introduces the calculus of discrete causal differential and formal models of causations. IA enables artificial intelligence and computational intelligent systems to mimic human inference abilities by cognitive computing. A wide range of applications of IA are identified and demonstrated in cognitive informatics and computational intelligence towards novel theories and technologies for machine-enabled inferences and reasoning. This work is presented in two parts. The inference operators of IA as well as their extensions and applications will be presented in this paper; while the structure of formal inference, the framework of IA, and the mathematical models of formal causations has been published in the first part of the paper in IJCINI 5(4).
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Michalewicz, Zbigniew, and Anthony Yeo. "Multiranges and Multitrackers in Statistical Databases." Fundamenta Informaticae 11, no. 1 (January 1, 1988): 41–48. http://dx.doi.org/10.3233/fi-1988-11104.

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The goal of statistical databases is to provide statistics about groups of individuals while protecting their privacy. Sometimes. by correlating enough statistics, sensitive data about individual can be inferred. The problem of protecting against such indirect disclosures of confidential data is called the inference problem and a protecting mechanism – an inference control. A good inference control mechanism should be effective (it should provide security to a reasonable extent) and feasible (a practical way exists to enforce it). At the same time it should retain the richness of the information revealed to the users. During the last few years several techniques were developed for controlling inferences. One of the earliest inference controls for statistical databases restricts the responses computed over too small or too large query-sets. However, this technique is easily subverted. In this paper we propose a new query-set size inference control which is based on the idea of multiranges and has better performance then the original one.
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Dissertations / Theses on the topic "Statistical inferences"

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Zhou, Haochuan. "Statistical Inferences for the Youden Index." Digital Archive @ GSU, 2011. http://digitalarchive.gsu.edu/math_diss/5.

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In diagnostic test studies, one crucial task is to evaluate the diagnostic accuracy of a test. Currently, most studies focus on the Receiver Operating Characteristics Curve and the Area Under the Curve. On the other hand, the Youden index, widely applied in practice, is another comprehensive measurement for the performance of a diagnostic test. For a continuous-scale test classifying diseased and non-diseased groups, finding the Youden index of the test is equivalent to maximize the sum of sensitivity and specificity for all the possible values of the cut-point. This dissertation concentrates on statistical inferences for the Youden index. First, an auxiliary tool for the Youden index, called the diagnostic curve, is defined and used to evaluate the diagnostic test. Second, in the paired-design study to assess the diagnostic accuracy of two biomarkers, the difference in paired Youden indices frequently acts as an evaluation standard. We propose an exact confidence interval for the difference in paired Youden indices based on generalized pivotal quantities. A maximum likelihood estimate-based interval and a bootstrap-based interval are also included in the study. Third, for certain diseases, an intermediate level exists between diseased and non-diseased status. With such concern, we define the Youden index for three ordinal groups, propose the empirical estimate of the Youden index, study the asymptotic properties of the empirical Youden index estimate, and construct parametric and nonparametric confidence intervals for the Youden index. Finally, since covariates often affect the accuracy of a diagnostic test, therefore, we propose estimates for the Youden index with a covariate adjustment under heteroscedastic regression models for the test results. Asymptotic properties of the covariate-adjusted Youden index estimators are investigated under normal error and non-normal error assumptions.
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Zhao, Ming. "Some Topics on Semiparametric Statistical Inferences." University of Toledo / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1341621928.

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Meng, Liang. "Statistical inferences of biophysical neural models." Thesis, Boston University, 2013. https://hdl.handle.net/2144/12819.

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Thesis (Ph.D.)--Boston University PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you.
A fundamental issue in neuroscience is to understand the dynamic properties of, and biological mechanisms underlying, neural spiking activity. Two types of approaches have been developed: statistical and biophysical modeling. Statistical models focus on describing simple relationships between observed neural spiking activity and the signals that the brain encodes. Biophysical models concentrate on describing the biological mechanisms underlying the generation of spikes. Despite a large body of work, there remains an unbridged gap between the two model types. In this thesis, we propose a statistical framework linking observed spiking patterns to a general class of dynamic neural models. The framework uses a sequential Monte Carlo, or particle filtering, method to efficiently explore the parameter space of a detailed dynamic or biophysical model. We utilize point process theory to develop a procedure for estimating parameters and hidden variables in neuronal biophysical models given only the observed spike times. We successfully implement this method for simulated examples and address the issues of model identification and misspecification. We then apply the particle filter to actual spiking data recorded from rat layer V cortical neurons and show that it correctly identifies the dynamics of a non-traditional, intrinsic current. The method succeeds even though the observed cells exhibit two distinct classes of spiking activity: regular spiking and bursting. We propose that the approach can also frame hypotheses of underlying intrinsic currents that can be directly tested by current or future biological and/or psychological experiments. We then demonstrate the application of the proposed method to a separate problem: constructing a hypothesis test to investigate whether a point process is generated by a constant or dynamically varying intensity function. The hypothesis is formulated as an autoregressive state space model, which reduces the testing problem to a test on the variance of the state process. We apply the particle filtering method to compute test statistics and identify the rejection region. A simulation study is performed to quantify the power of this test procedure. Ultimately, the modeling framework and estimation procedures we developed provide a successful link between dynamical neural models and statistical inference from spike train data.
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Sharghi, Sima. "Statistical inferences for missing data/causal inferences based on modified empirical likelihood." Bowling Green State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1624823412604593.

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Grossman, Jason. "Inferences from observations to simple statistical hypotheses." Phd thesis, Department of Philosophy, 2005. http://hdl.handle.net/2123/9107.

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Zhang, Shiju. "Statistical Inferences under a semiparametric finite mixture model." See Full Text at OhioLINK ETD Center (Requires Adobe Acroba Reader for viewing), 2005. http://www.ohiolink.edu/etd/view.cgi?toledo1135779503.

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Thesis (Ph.D.)--University of Toledo, 2005.
Typescript. "A dissertation [submitted] as partial fulfillment of the requirements of the Doctor of Philosophy degree in Mathematics." Bibliography: leaves 100-105.
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Stewart, Patrick. "Statistical Inferences on Inflated Data Based on Modified Empirical Likelihood." Bowling Green State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1590455262157706.

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Lu, Tsui-Shan Zhou Haibo. "Statistical inferences for outcome dependent sampling design with multivariate outcomes." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2009. http://dc.lib.unc.edu/u?/etd,2447.

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Thesis (Ph. D.)--University of North Carolina at Chapel Hill, 2009.
Title from electronic title page (viewed Sep. 3, 2009). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Biostatistics, Gillings School of Global Public Health." "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Biostatistics, Gillings School of Global Public Health." Discipline: Biostatistics; Department/School: Public Health.
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Fan, Cong Cong Michelle. "A multiplicative model of the transmission rate and its statistical inferences." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/NQ63595.pdf.

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Wang, Xing. "Inferences about Parameters of Trivariate Normal Distribution with Missing Data." FIU Digital Commons, 2013. http://digitalcommons.fiu.edu/etd/933.

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Multivariate normal distribution is commonly encountered in any field, a frequent issue is the missing values in practice. The purpose of this research was to estimate the parameters in three-dimensional covariance permutation-symmetric normal distribution with complete data and all possible patterns of incomplete data. In this study, MLE with missing data were derived, and the properties of the MLE as well as the sampling distributions were obtained. A Monte Carlo simulation study was used to evaluate the performance of the considered estimators for both cases when ρ was known and unknown. All results indicated that, compared to estimators in the case of omitting observations with missing data, the estimators derived in this article led to better performance. Furthermore, when ρ was unknown, using the estimate of ρ would lead to the same conclusion.
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Books on the topic "Statistical inferences"

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Miller, Douglas R. Making statistical inferences about software reliability. Hampton, Va: Langley Research Center, 1988.

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United States. National Aeronautics and Space Administration. Scientific and Technical Information Division., ed. Making statistical inferences about software reliability. [Washington, DC]: National Aeronautics and Space Administration, Scientific and Technical Information Division, 1988.

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MacRae, Sandy. Drawing inferences from statistical data: Tutor notes. Leicester: British Psychological Society, 1994.

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Uebersax, John. Validity inferences from interobserver agreement. Santa Monica, CA: Rand, 1989.

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Howard, Wainer, and Educational Testing Service, eds. Drawing inferences from self-selected samples. Mahwah, N.J: Lawrence Erlbaum Associates, 2000.

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He, Hua, Pan Wu, and Ding-Geng Chen, eds. Statistical Causal Inferences and Their Applications in Public Health Research. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41259-7.

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Gaba, Anil. Using survey data in inferences about purchase behaviour. Fontainebleau, France: INSEAD, 1990.

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Pelosi, Marilyn K. Doing statistics for business with Excel: Data, inferences, and decision making. New York: John Wiley, 1999.

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Schklar, Jason. Who killed Mrs. Prob(ability)'s dog?: Drawing inferences from statistical evidence. Chicago, Ill: American Bar Foundation, 1998.

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W, Roberts Carl, ed. Text analysis for the social sciences: Methods for drawing statistical inferences from texts and transcripts. Mahwah NJ: Erlbaum, 1997.

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Book chapters on the topic "Statistical inferences"

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Blanchet, Gérard, and Maurice Charbit. "Statistical Inferences." In Digital Signal and Image Processing Using MATLAB®, 25–84. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2015. http://dx.doi.org/10.1002/9781119054009.ch2.

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Antia, H. M. "Statistical Inferences." In Numerical Methods for Scientists and Engineers, 401–24. Gurgaon: Hindustan Book Agency, 2012. http://dx.doi.org/10.1007/978-93-86279-52-1_9.

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Rahman, Azizur, Faruq Abdulla, and Md Moyazzem Hossain. "Statistical Inferences." In Scientific Data Analysis with R, 207–45. Boca Raton: Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003426189-7.

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Cleophas, Ton J., Aeilko H. Zwinderman, and Toine F. Cleophas. "Multiple Statistical Inferences." In Statistics Applied to Clinical Trials, 51–58. Dordrecht: Springer Netherlands, 2000. http://dx.doi.org/10.1007/978-94-015-9508-7_7.

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Cleophas, Ton J., Aeilko H. Zwinderman, and Toine F. Cleophas. "Multiple Statistical Inferences." In Statistics Applied to Clinical Trials, 87–96. Dordrecht: Springer Netherlands, 2006. http://dx.doi.org/10.1007/978-1-4020-4650-6_7.

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Cleophas, Ton J., and Aeilko H. Zwinderman. "Multiple Statistical Inferences." In Statistics Applied to Clinical Studies, 109–17. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-2863-9_9.

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Cleophas, Ton J., Aeilko H. Zwinderman, and Toine F. Cleophas. "Multiple Statistical Inferences." In Statistics Applied to Clinical Trials, 73–82. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0337-7_7.

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Hahs-Vaughn, Debbie L., and Richard G. Lomax. "Inferences About Proportions." In Statistical Concepts, 297–343. New York, NY : Routledge, 2019.: Routledge, 2020. http://dx.doi.org/10.4324/9780429261268-8.

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Hahs-Vaughn, Debbie L., and Richard G. Lomax. "Inferences About Variances." In Statistical Concepts, 345–68. New York, NY : Routledge, 2019.: Routledge, 2020. http://dx.doi.org/10.4324/9780429261268-9.

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Nagadevara, Vishnuprasad. "Statistical Methods: Basic Inferences." In International Series in Operations Research & Management Science, 137–78. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-68837-4_6.

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Conference papers on the topic "Statistical inferences"

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Leshinskaya, Anna, and Sharon L. Thompson-Schill. "Inferences about Uniqueness in Statistical Learning." In 2018 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2018. http://dx.doi.org/10.32470/ccn.2018.1115-0.

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Huber, Wayne C. "Analysis of BMP Quality Data: Inferences from Statistical Tests." In World Environmental and Water Resources Congress 2007. Reston, VA: American Society of Civil Engineers, 2007. http://dx.doi.org/10.1061/40927(243)53.

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Prodromou, Theodosia. "Students’ emerging expressions of uncertainty while making informal statistical inferences about data." In Statistics education for Progress: Youth and Official Statistics. IASE international Association for Statistical Education, 2013. http://dx.doi.org/10.52041/srap.13204.

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This research study investigates the development of middle school students’ emerging expressions of uncertainty through observation of 14- to 15-year-olds, challenged in informal inferential reasoning. This study focuses on students’ investigations when sampling from populations and using information from the samples to draw conclusions about the parent populations. The results suggest that when the students engaged in processes of drawing generalised conclusions from data, involving generalising beyond data and using data as evidence of the generalisation, they developed probabilistic language to articulate the degree of certainty embedded in the generalisation. As the students engaged in their inquiries, they developed more sophisticated expressions of the probabilistic language. Attending to students’ emerging articulations of uncertainty when making judgments about the underlying structure of the data and observing patterns and trends in data, provides an opportunity to develop more sophisticated understandings of the developmental process of students’ statistical inferential reasoning.
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Tanaka, Kazuyuki, Takafumi Usui, and Muneki Yasuda. "Statistical Inferences by Gaussian Markov Random Fields on Complex Networks." In 2008 International Conference on Computational Intelligence for Modelling Control & Automation. IEEE, 2008. http://dx.doi.org/10.1109/cimca.2008.14.

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Herrera-Bennett, Arianne. "Improving Statistical Inferences: MOOC Enhances Conceptual Understanding Among Online Learners." In 2019 AERA Annual Meeting. Washington DC: AERA, 2019. http://dx.doi.org/10.3102/1446221.

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Chen, Zixi. "The Robustness of Statistical Inferences With Latent Levels of Clusters." In 2020 AERA Annual Meeting. Washington DC: AERA, 2020. http://dx.doi.org/10.3102/1575938.

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GUO, RENKUAN, and ERNIE LOVE. "FUZZY SET-VALUED STATISTICAL INFERENCES ON A SYSTEM OPERATING DATA." In Proceedings of the 2004 Asian International Workshop (AIWARM 2004). WORLD SCIENTIFIC, 2004. http://dx.doi.org/10.1142/9789812702685_0022.

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Ntozi, James, and George Kibirige. "Three decades of training government statistical staff in developing countries: the African experience." In Proceedings of the First Scientific Meeting of the IASE. International Association for Statistical Education, 1993. http://dx.doi.org/10.52041/srap.93402.

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Almost all official statistical services in Africa were introduced during the colonial era with the setting up of statistical units in the colonies and territories whose functions were largely determined by the ruling colonial powers. In East Africa, the service was set up in 1926 as the Statistical Section of the East African Governors' Conference with a mandate of "collecting statistics gradually, on the same method, throughout the territories, and to tabulate and compare results so that true inferences can be drawn" (Singh, 1971). Nigeria's statistical office was established in 1947 wither personnel deployed from the Treasury, Customs and Office of the Chief Secretary.
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Roussel, Stephane, Hemanth Porumamilla, Charles Birdsong, Peter Schuster, and Christopher Clark. "Enhanced Vehicle Identification Utilizing Sensor Fusion and Statistical Algorithms." In ASME 2009 International Mechanical Engineering Congress and Exposition. ASMEDC, 2009. http://dx.doi.org/10.1115/imece2009-12012.

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Several studies in the area of vehicle detection and identification involve the use of probabilistic analysis and sensor fusion. While several sensors utilized for identifying vehicle presence and proximity have been researched, their effectiveness in identifying vehicle types has remained inadequate. This study presents the utilization of an ultrasonic sensor coupled with a magnetic sensor and the development of statistical algorithms to overcome this limitation. Mathematical models of both the ultrasonic and magnetic sensors were constructed to first understand the intrinsic characteristics of the individual sensors and also to provide a means of simulating the performance of the combined sensor system and to facilitate algorithm development. Preliminary algorithms that utilized this sensor fusion were developed to make inferences relating to vehicle proximity as well as type. It was noticed that while it helped alleviate the limitations of the individual sensors, the algorithm was affected by high occurrences of false positives. Also, since sensors carry only partial information about the surrounding environment and their measured quantities are partially corrupted with noise, probabilistic techniques were employed to extend the preliminary algorithms to include these sensor characteristics. These statistical techniques were utilized to reconstruct partial state information provided by the sensors and to also filter noisy measurement data. This probabilistic approach helped to effectively utilize the advantages of sensor fusion to further enhance the reliability of inferences made on vehicle identification. In summary, the study investigated the enhancement of vehicle identification through the use of sensor fusion and statistical techniques. The algorithms developed showed encouraging results in alleviating the occurrences of false positive inferences. One of the several applications of this study is in the use of ultrasonic-magnetic sensor combination for advanced traffic monitoring such as smart toll booths.
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Alston-Knox, Clair Louise, Christopher Mark Strickland, Theo Gazos, and Kerrie Lee Mengersen. "Teaching and Learning in Statistics: Harnessing the power of modern statistical software to improve students statistical reasoning and thinking." In Fifth International Conference on Higher Education Advances. Valencia: Universitat Politècnica València, 2019. http://dx.doi.org/10.4995/head19.2019.9239.

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The reproducibility crisis in science has launched global discussion about the need to restructure the way statistics is taught across a wide range of disciplines. While this need has been recognized and discussed in the academic community for many years, the impetus for educational reform of statistics was boosted by Ioannidis (2005), which resulted in a great deal of attention on issues regarding the inappropriate use of statistical reasoning. The availability of data across business and research has increased dramatically in recent years. This access to data has resulted in almost every member of society needing a skill set that allows them to think critically about the inferences that can validly be drawn to improve decisions based on data. One way of improving statistical literacy and thinking is through the identification and use of appropriate statistical software that will allow students, and other practitioners with basic training, access to modern statistical modeling techniques on a platform that allows them to focus on outcomes. A key component of using AutoStat for teaching statistical thinking is in alleviating the need for coding, which allows the instructors to focus on key concepts, questions and outcomes.
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Reports on the topic "Statistical inferences"

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Zang, Emma. Bayesian Statistics for Social and Health Scientists in R and Python. Instats Inc., 2023. http://dx.doi.org/10.61700/obtt1o65iw3ui469.

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This seminar will introduce you to Bayesian statistics, which are increasingly popular and offer a powerful alternative to more traditional forms of statistical analysis. Targeted at a social and health science audience, the seminar will cover the fundamentals of Bayesian inference and illustrate a variety of techniques with applied examples of Bayesian regressions and hierarchical models. You will gain an understanding of Markov chain Monte Carlo (MCMC) methods and learn how to develop and validate Bayesian models so that you can apply them in your daily research, with the kinds of intuitive inferences that Bayesian methods allow. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS Equivalent points.
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Zang, Emma. Bayesian Statistics for Social and Health Scientists in R and Python + 2 Free Seminars. Instats Inc., 2022. http://dx.doi.org/10.61700/bgfpomu3wdhe5469.

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This seminar will introduce you to Bayesian statistics, which are increasingly popular and offer a powerful alternative to more traditional forms of statistical analysis. Targeted at a social and health science audience, the seminar will cover the fundamentals of Bayesian inference and illustrate a variety of techniques with applied examples of Bayesian regressions and hierarchical models. You will gain an understanding of Markov chain Monte Carlo (MCMC) methods and learn how to develop and validate Bayesian models so that you can apply them in your daily research, with the kinds of intuitive inferences that Bayesian methods allow. When purchasing the seminar you will be freely enrolled in two on-demand seminars for Path Analysis in R and CFA/SEM in R with Bayesian estimation by Professor Zyphur, helping you to extend your learning and offering a substantial value. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS Equivalent points.
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Maeno, Yoshiharu. Epidemiological geographic profiling for a meta-population network. Web of Open Science, December 2020. http://dx.doi.org/10.37686/ser.v1i2.78.

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Epidemiological geographic profiling is a statistical method for making inferences about likely areas of a source from the geographical distribution of patients. Epidemiological geographic profiling algorithms are developed to locate a source from the dataset on the number of new cases for a meta-population network model. It is found from the WHO dataset on the SARS outbreak that Hong Kong remains the most likely source throughout the period of observation. This reasoning is pertinent under the restricted circumstance that the number of reported probable cases in China was missing, unreliable, and incomprehensive. It may also imply that globally connected Hong Kong was more influential as a spreader than China. Singapore, Taiwan, Canada, and the United States follow Hong Kong in the likeliness ranking list
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Carroll, Raymond J. Research in Statistical Inference. Fort Belvoir, VA: Defense Technical Information Center, August 1991. http://dx.doi.org/10.21236/ada252928.

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5

Manski, Charles F. Remarks on statistical inference for statistical decisions. The IFS, January 2019. http://dx.doi.org/10.1920/wp.cem.2019.06.

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Manski, Charles F. Remarks on statistical inference for statistical decisions. The IFS, January 2019. http://dx.doi.org/10.1920/wp.cem.2019.0619.

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7

Karr, Alan F. Statistical Inference for Stochastic Processes. Fort Belvoir, VA: Defense Technical Information Center, October 1987. http://dx.doi.org/10.21236/ada190491.

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Masry, Elias. Statistical Inference from Sampled Data. Fort Belvoir, VA: Defense Technical Information Center, May 1998. http://dx.doi.org/10.21236/ada342544.

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Gimpel, K., and D. Rudoy. Statistical Inference in Graphical Models. Fort Belvoir, VA: Defense Technical Information Center, June 2008. http://dx.doi.org/10.21236/ada482530.

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Batchelder, William H. Statistical Inference for Cultural Consensus Theory. Fort Belvoir, VA: Defense Technical Information Center, February 2014. http://dx.doi.org/10.21236/ada605989.

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