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Probability Matching Priors for the Bivariate Normal Distribution

2008
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Release : 2008
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Book Synopsis Probability Matching Priors for the Bivariate Normal Distribution by : Upasana Santra

Download or read book Probability Matching Priors for the Bivariate Normal Distribution written by Upasana Santra. This book was released on 2008. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Probability Matching Priors: Higher Order Asymptotics

2012-12-06 Mathematics
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Release : 2012-12-06
Genre : Mathematics
Kind : eBook
Book Rating : 36X/5 ( reviews)

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Book Synopsis Probability Matching Priors: Higher Order Asymptotics by : Gauri Sankar Datta

Download or read book Probability Matching Priors: Higher Order Asymptotics written by Gauri Sankar Datta. This book was released on 2012-12-06. Available in PDF, EPUB and Kindle. Book excerpt: This is the first book on the topic of probability matching priors. It targets researchers, Bayesian and frequentist; graduate students in Statistics.

The Bivariate Normal Probability Distribution

1957 Distribution (Probability theory)
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Release : 1957
Genre : Distribution (Probability theory)
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Book Synopsis The Bivariate Normal Probability Distribution by : Donald Bruce Owen

Download or read book The Bivariate Normal Probability Distribution written by Donald Bruce Owen. This book was released on 1957. Available in PDF, EPUB and Kindle. Book excerpt:

Applied Statistical Science III

1998 Mathematics
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Release : 1998
Genre : Mathematics
Kind : eBook
Book Rating : 817/5 ( reviews)

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Book Synopsis Applied Statistical Science III by : Mohammad Ahsanullah

Download or read book Applied Statistical Science III written by Mohammad Ahsanullah. This book was released on 1998. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Frontiers of Statistical Decision Making and Bayesian Analysis

2010-07-24 Mathematics
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Release : 2010-07-24
Genre : Mathematics
Kind : eBook
Book Rating : 446/5 ( reviews)

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Book Synopsis Frontiers of Statistical Decision Making and Bayesian Analysis by : Ming-Hui Chen

Download or read book Frontiers of Statistical Decision Making and Bayesian Analysis written by Ming-Hui Chen. This book was released on 2010-07-24. Available in PDF, EPUB and Kindle. Book excerpt: 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.

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