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Literatura académica sobre el tema "Bayesian Sample size"

Autor: Grafiati

Publicado: 10 de marzo de 2023

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Tesis sobre el tema "Bayesian Sample size"

1

Cámara, Hagen Luis Tomás. "A consensus based Bayesian sample size criterion." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/MQ64329.pdf.

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Cheng, Dunlei Stamey James D. "Topics in Bayesian sample size determination and Bayesian model selection." Waco, Tex. : Baylor University, 2007. http://hdl.handle.net/2104/5039.

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3

Islam, A. F. M. Saiful. "Loss functions, utility functions and Bayesian sample size determination." Thesis, Queen Mary, University of London, 2011. http://qmro.qmul.ac.uk/xmlui/handle/123456789/1259.

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This thesis consists of two parts. The purpose of the first part of the research is to obtain Bayesian sample size determination (SSD) using loss or utility function with a linear cost function. A number of researchers have studied the Bayesian SSD problem. One group has considered utility (loss) functions and cost functions in the SSD problem and others not. Among the former most of the SSD problems are based on a symmetrical squared error (SE) loss function. On the other hand, in a situation when underestimation is more serious than overestimation or vice-versa, then an asymmetric loss funct

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4

M'lan, Cyr Emile. "Bayesian sample size calculations for cohort and case-control studies." Thesis, McGill University, 2002. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=82923.

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Sample size determination is one of the most important statistical issues in the early stages of any investigation that anticipates statistical analyses.<br>In this thesis, we examine Bayesian sample size determination methodology for interval estimation. Four major epidemiological study designs, cohort, case-control, cross-sectional and matched pair are the focus. We study three Bayesian sample size criteria: the average length criterion (ALC), the average coverage criterion ( ACC) and the worst outcome criterion (WOC ) as well as various extensions of these criteria. In addition, a si

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5

Banton, Dwaine Stephen. "A BAYESIAN DECISION THEORETIC APPROACH TO FIXED SAMPLE SIZE DETERMINATION AND BLINDED SAMPLE SIZE RE-ESTIMATION FOR HYPOTHESIS TESTING." Diss., Temple University Libraries, 2016. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/369007.

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Statistics<br>Ph.D.<br>This thesis considers two related problems that has application in the field of experimental design for clinical trials: • fixed sample size determination for parallel arm, double-blind survival data analysis to test the hypothesis of no difference in survival functions, and • blinded sample size re-estimation for the same. For the first problem of fixed sample size determination, a method is developed generally for testing of hypothesis, then applied particularly to survival analysis; for the second problem of blinded sample size re-estimation, a method is developed spe

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6

Tan, Say Beng. "Bayesian decision theoretic methods for clinical trials." Thesis, Imperial College London, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.312988.

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7

Safaie, Nasser. "A fully Bayesian approach to sample size determination for verifying process improvement." Diss., Wichita State University, 2010. http://hdl.handle.net/10057/3656.

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There has been significant growth in the development and application of Bayesian methods in industry. The Bayes’ theorem describes the process of learning from experience and shows how knowledge about the state of nature is continually modified as new data become available. This research is an effort to introduce the Bayesian approach as an effective tool for evaluating process adjustments aimed at causing a change in a process parameter. This is usually encountered in scenarios where the process is found to be stable but operating away from the desired level. In these scenarios, a number of c

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8

Kaouache, Mohammed. "Bayesian modeling of continuous diagnostic test data: sample size and Polya trees." Thesis, McGill University, 2012. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=107833.

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Parametric models such as the bi-normal have been widely used to analyse datafrom imperfect continuous diagnostic tests. Such models rely on assumptions thatmay often be unrealistic and/or unveri_able, and in such cases nonparametric modelspresent an attractive alternative. Further, even when normality holds, researcherstend to underestimate the sample size required to accurately estimate disease preva-lence from bi-normal models when densities from diseased and non-diseased subjectsoverlap. In this thesis we investigate both of these problems. First, we study theuse of nonparametric Polya tre

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9

Ma, Junheng. "Contributions to Numerical Formal Concept Analysis, Bayesian Predictive Inference and Sample Size Determination." Case Western Reserve University School of Graduate Studies / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=case1285341426.

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10

Kikuchi, Takashi. "A Bayesian cost-benefit approach to sample size determination and evaluation in clinical trials." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:f5cb4e27-8d4c-4a80-b792-469e50efeea2.

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Current practice for sample size computations in clinical trials is largely based on frequentist or classical methods. These methods have the drawback of requiring a point estimate of the variance of treatment effect and are based on arbitrary settings of type I and II errors. They also do not directly address the question of achieving the best balance between the costs of the trial and the possible benefits by using a new medical treatment, and fail to consider the important fact that the number of users depends on evidence for improvement compared with the current treatment. A novel Bayesian

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