Probability Matching Priors for the Bivariate Normal Distribution PDF Download
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Author: Upasana Santra
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
There however, does not exist a prior that satisfies the matching via distribution functions criterion in this case. Finally, a general class of priors have been obtained for inference about the ratio of standard deviations. The propriety of the resultant posteriors is proved in each case under mild conditions and simulation results suggest that the approximations are valid even for moderate sample sizes. Further, several likelihood based methods have been considered for the correlation coefficient. One common feature of all these modified likelihoods is that they are all dependent on the data only through the sample correlation coefficient r.
Author: Upasana Santra
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
There however, does not exist a prior that satisfies the matching via distribution functions criterion in this case. Finally, a general class of priors have been obtained for inference about the ratio of standard deviations. The propriety of the resultant posteriors is proved in each case under mild conditions and simulation results suggest that the approximations are valid even for moderate sample sizes. Further, several likelihood based methods have been considered for the correlation coefficient. One common feature of all these modified likelihoods is that they are all dependent on the data only through the sample correlation coefficient r.
Author: Gauri Sankar Datta
Publisher: Springer Science & Business Media
ISBN: 146122036X
Category : Mathematics
Languages : en
Pages : 138
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Book Description
This is the first book on the topic of probability matching priors. It targets researchers, Bayesian and frequentist; graduate students in Statistics.
Author: Donald Bruce Owen
Publisher:
ISBN:
Category : Distribution (Probability theory)
Languages : en
Pages : 140
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Book Description
Author: Mohammad Ahsanullah
Publisher:
ISBN: 9781560725817
Category : Mathematics
Languages : en
Pages : 458
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Book Description
CONTENTS: Partially Adaptive Rank and Regression Rank Scores Tests in Linear Models; An Analysis of Nonoparametric Smoothers; Supercritical Branching Random Walk in D-Dimensional Random Environment; Lack of Fit Tests in Regression With Non-Random Design; Asymptotics of the Deepest Line; Multivariate Rank Statistics Processes and Change Point Analysis; Improved Estimation of the Parameters of an Autoagressive Gaussian Process Under Uncertain Restrictions; Testing Normality For Censored Data; Large Sample theory For Estimators of the Moments Based On Synthetic Data Under Randomly Right-Censoring; The Stein Phenomenon in Simultaneous Estimation: A Review; Two Techniques of Integration By Parts and Some Applications; Conditional Confidence Intervals of Regression Coefficients Following Rejection of Preliminary Test; Order Preserving Estimators of Eigenvalues of the Scale Matrix in the Multivariate F Distribution Under Stein's Loss Function; Sequential Estimation of the Man of An Exponential Distribution Via Partial Piece Wise Sampling; Recent Developments on Probability Matching Priors; On the Informative Presentation of Likelihood; Bahadur Risk, Exponential Families and Recursive Estimation; Some Quick Estimators Based on Sample Maxima; Inferences of Power Function Distribution Based on Ordered Random Variables; Estimation of the Location Parameter of A Cauchy Distribution Using A Ranked Set Sample; On A Delayed Service Queuing System With Random Server Capacity and Impatient Customers; Canonical Co-ordinated for Graphical Representation of Multivariate Data; Some Single Use Confidence Regions in Multivariate Calibration Problem; The Likelihood Ratio Test of Non-Nested Linear Regression Models; Exact Power of Classical Tests for Bivariate Linear Hypothesis; Characterisation of the Gamma and the Complex Case Wishart Densities; Jack-knife and Robust Estimation for the Parameters in Pharmocokinetes.
Author: Ming-Hui Chen
Publisher: Springer Science & Business Media
ISBN: 1441969446
Category : Mathematics
Languages : en
Pages : 631
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Book Description
Research in Bayesian analysis and statistical decision theory is rapidly expanding and diversifying, making it increasingly more difficult for any single researcher to stay up to date on all current research frontiers. This book provides a review of current research challenges and opportunities. While the book can not exhaustively cover all current research areas, it does include some exemplary discussion of most research frontiers. Topics include objective Bayesian inference, shrinkage estimation and other decision based estimation, model selection and testing, nonparametric Bayes, the interface of Bayesian and frequentist inference, data mining and machine learning, methods for categorical and spatio-temporal data analysis and posterior simulation methods. Several major application areas are covered: computer models, Bayesian clinical trial design, epidemiology, phylogenetics, bioinformatics, climate modeling and applications in political science, finance and marketing. As a review of current research in Bayesian analysis the book presents a balance between theory and applications. The lack of a clear demarcation between theoretical and applied research is a reflection of the highly interdisciplinary and often applied nature of research in Bayesian statistics. The book is intended as an update for researchers in Bayesian statistics, including non-statisticians who make use of Bayesian inference to address substantive research questions in other fields. It would also be useful for graduate students and research scholars in statistics or biostatistics who wish to acquaint themselves with current research frontiers.
Author: Gauri Sankar Datta
Publisher:
ISBN: 9781461220374
Category :
Languages : en
Pages : 144
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Book Description
Author: United States. National Bureau of Standards
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 312
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Book Description
Author: Jim Albert
Publisher: CRC Press
ISBN: 1351030132
Category : Mathematics
Languages : en
Pages : 553
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Book Description
Probability and Bayesian Modeling is an introduction to probability and Bayesian thinking for undergraduate students with a calculus background. The first part of the book provides a broad view of probability including foundations, conditional probability, discrete and continuous distributions, and joint distributions. Statistical inference is presented completely from a Bayesian perspective. The text introduces inference and prediction for a single proportion and a single mean from Normal sampling. After fundamentals of Markov Chain Monte Carlo algorithms are introduced, Bayesian inference is described for hierarchical and regression models including logistic regression. The book presents several case studies motivated by some historical Bayesian studies and the authors’ research. This text reflects modern Bayesian statistical practice. Simulation is introduced in all the probability chapters and extensively used in the Bayesian material to simulate from the posterior and predictive distributions. One chapter describes the basic tenets of Metropolis and Gibbs sampling algorithms; however several chapters introduce the fundamentals of Bayesian inference for conjugate priors to deepen understanding. Strategies for constructing prior distributions are described in situations when one has substantial prior information and for cases where one has weak prior knowledge. One chapter introduces hierarchical Bayesian modeling as a practical way of combining data from different groups. There is an extensive discussion of Bayesian regression models including the construction of informative priors, inference about functions of the parameters of interest, prediction, and model selection. The text uses JAGS (Just Another Gibbs Sampler) as a general-purpose computational method for simulating from posterior distributions for a variety of Bayesian models. An R package ProbBayes is available containing all of the book datasets and special functions for illustrating concepts from the book. A complete solutions manual is available for instructors who adopt the book in the Additional Resources section.
Author: George Powell Steck
Publisher:
ISBN:
Category : Distribution (Probability theory)
Languages : en
Pages : 18
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Book Description
Author: Mohammad Ahsanullah
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 458
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Book Description
CONTENTS: Partially Adaptive Rank and Regression Rank Scores Tests in Linear Models; An Analysis of Nonoparametric Smoothers; Supercritical Branching Random Walk in D-Dimensional Random Environment; Lack of Fit Tests in Regression With Non-Random Design; Asymptotics of the Deepest Line; Multivariate Rank Statistics Processes and Change Point Analysis; Improved Estimation of the Parameters of an Autoagressive Gaussian Process Under Uncertain Restrictions; Testing Normality For Censored Data; Large Sample theory For Estimators of the Moments Based On Synthetic Data Under Randomly Right-Censoring; The Stein Phenomenon in Simultaneous Estimation: A Review; Two Techniques of Integration By Parts and Some Applications; Conditional Confidence Intervals of Regression Coefficients Following Rejection of Preliminary Test; Order Preserving Estimators of Eigenvalues of the Scale Matrix in the Multivariate F Distribution Under Stein's Loss Function; Sequential Estimation of the Man of An Exponential Distribution Via Partial Piece Wise Sampling; Recent Developments on Probability Matching Priors; On the Informative Presentation of Likelihood; Bahadur Risk, Exponential Families and Recursive Estimation; Some Quick Estimators Based on Sample Maxima; Inferences of Power Function Distribution Based on Ordered Random Variables; Estimation of the Location Parameter of A Cauchy Distribution Using A Ranked Set Sample; On A Delayed Service Queuing System With Random Server Capacity and Impatient Customers; Canonical Co-ordinated for Graphical Representation of Multivariate Data; Some Single Use Confidence Regions in Multivariate Calibration Problem; The Likelihood Ratio Test of Non-Nested Linear Regression Models; Exact Power of Classical Tests for Bivariate Linear Hypothesis; Characterisation of the Gamma and the Complex Case Wishart Densities; Jack-knife and Robust Estimation for the Parameters in Pharmocokinetes.
