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Constrained Statistical Inference


Constrained Statistical Inference
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Constrained Statistical Inference


Constrained Statistical Inference
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Author : Pranab Kumar Sen
language : en
Publisher:
Release Date : 2011

Constrained Statistical Inference written by Pranab Kumar Sen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.




Constrained Statistical Inference


Constrained Statistical Inference
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Author : Mervyn J. Silvapulle
language : en
Publisher: John Wiley & Sons
Release Date : 2011-09-15

Constrained Statistical Inference written by Mervyn J. Silvapulle and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-09-15 with Mathematics categories.


An up-to-date approach to understanding statistical inference Statistical inference is finding useful applications in numerous fields, from sociology and econometrics to biostatistics. This volume enables professionals in these and related fields to master the concepts of statistical inference under inequality constraints and to apply the theory to problems in a variety of areas. Constrained Statistical Inference: Order, Inequality, and Shape Constraints provides a unified and up-to-date treatment of the methodology. It clearly illustrates concepts with practical examples from a variety of fields, focusing on sociology, econometrics, and biostatistics. The authors also discuss a broad range of other inequality-constrained inference problems that do not fit well in the contemplated unified framework, providing a meaningful way for readers to comprehend methodological resolutions. Chapter coverage includes: Population means and isotonic regression Inequality-constrained tests on normal means Tests in general parametric models Likelihood and alternatives Analysis of categorical data Inference on monotone density function, unimodal density function, shape constraints, and DMRL functions Bayesian perspectives, including Stein’s Paradox, shrinkage estimation, and decision theory



Constrained Statistical Inference


Constrained Statistical Inference
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Author : Mervyn J. Silvapulle
language : en
Publisher: Wiley-Interscience
Release Date : 2004-11-08

Constrained Statistical Inference written by Mervyn J. Silvapulle and has been published by Wiley-Interscience this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-11-08 with Mathematics categories.


An up-to-date approach to understanding statistical inference Statistical inference is finding useful applications in numerous fields, from sociology and econometrics to biostatistics. This volume enables professionals in these and related fields to master the concepts of statistical inference under inequality constraints and to apply the theory to problems in a variety of areas. Constrained Statistical Inference: Order, Inequality, and Shape Constraints provides a unified and up-to-date treatment of the methodology. It clearly illustrates concepts with practical examples from a variety of fields, focusing on sociology, econometrics, and biostatistics. The authors also discuss a broad range of other inequality-constrained inference problems that do not fit well in the contemplated unified framework, providing a meaningful way for readers to comprehend methodological resolutions. Chapter coverage includes: Population means and isotonic regression Inequality-constrained tests on normal means Tests in general parametric models Likelihood and alternatives Analysis of categorical data Inference on monotone density function, unimodal density function, shape constraints, and DMRL functions Bayesian perspectives, including Stein’s Paradox, shrinkage estimation, and decision theory



Constrained Statistical Inference


Constrained Statistical Inference
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Author : David K. Ruch
language : en
Publisher:
Release Date : 2005

Constrained Statistical Inference written by David K. Ruch and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Digital images categories.


1. Introduction -- 2. Comparison of population means and isotonic regression -- 3. Tests on multivariate normal mean -- 4. Tests in general parametric models -- 5. Likelihood and alternatives -- 6. Analysis of categorical data -- 7. Beyond parametrics -- 8. Bayesian perspectives -- 9. Miscellaneous topics



Probability And Statistical Inference


Probability And Statistical Inference
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Author : Robert Bartoszynski
language : en
Publisher: John Wiley & Sons
Release Date : 2007-11-16

Probability And Statistical Inference written by Robert Bartoszynski and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-11-16 with Mathematics categories.


Now updated in a valuable new edition—this user-friendly book focuses on understanding the "why" of mathematical statistics Probability and Statistical Inference, Second Edition introduces key probability and statis-tical concepts through non-trivial, real-world examples and promotes the developmentof intuition rather than simple application. With its coverage of the recent advancements in computer-intensive methods, this update successfully provides the comp-rehensive tools needed to develop a broad understanding of the theory of statisticsand its probabilistic foundations. This outstanding new edition continues to encouragereaders to recognize and fully understand the why, not just the how, behind the concepts,theorems, and methods of statistics. Clear explanations are presented and appliedto various examples that help to impart a deeper understanding of theorems and methods—from fundamental statistical concepts to computational details. Additional features of this Second Edition include: A new chapter on random samples Coverage of computer-intensive techniques in statistical inference featuring Monte Carlo and resampling methods, such as bootstrap and permutation tests, bootstrap confidence intervals with supporting R codes, and additional examples available via the book's FTP site Treatment of survival and hazard function, methods of obtaining estimators, and Bayes estimating Real-world examples that illuminate presented concepts Exercises at the end of each section Providing a straightforward, contemporary approach to modern-day statistical applications, Probability and Statistical Inference, Second Edition is an ideal text for advanced undergraduate- and graduate-level courses in probability and statistical inference. It also serves as a valuable reference for practitioners in any discipline who wish to gain further insight into the latest statistical tools.



Constrained Statistical Inference In Regression


Constrained Statistical Inference In Regression
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Author : Thelge Buddika Peiris
language : en
Publisher:
Release Date : 2014

Constrained Statistical Inference In Regression written by Thelge Buddika Peiris and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with Regression analysis categories.


Regression analysis constitutes a large portion of the statistical repertoire in applications. In case where such analysis is used for exploratory purposes with no previous knowledge of the structure one would not wish to impose any constraints on the problem. But in many applications we are interested in a simple parametric model to describe the structure of a system with some prior knowledge of the structure. An important example of this occurs when the experimenter has the strong belief that the regression function changes monotonically in some or all of the predictor variables in a region of interest. The analyses needed for statistical inference under such constraints are nonstandard. The specific aim of this study is to introduce a technique which can be used for statistical inferences of a multivariate simple regression with some non-standard constraints.



Regression Analysis


Regression Analysis
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Author : Richard A. Berk
language : en
Publisher: SAGE Publications
Release Date : 2003-07-17

Regression Analysis written by Richard A. Berk and has been published by SAGE Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-07-17 with Social Science categories.


Berk has incisively identified the various strains of regression abuse and suggests practical steps for researchers who desire to do good social science while avoiding such errors." --Peter H. Rossi, University of Massachusetts, Amherst "I have been waiting for a book like this for some time. Practitioners, especially those doing applied work, will have much to gain from Berk′s volume, regardless of their level of statistical sophistication. Graduate students in sociology, education, public policy, and any number of similar fields should also use it. It will also be a useful foil for conventional texts for the teaching of the regression model. I plan to use it for my students as a text, and hope others will do the same." --Herbert Smith, Professor of Demography & Sociology, University of Pennsylvania Regression is often applied to questions for which it is ill equipped to answer. As a formal matter, conventional regression analysis does nothing more than produce from a data set a collection of conditional means and conditional variances. The problem, though, is that researchers typically want more: they want tests, confidence intervals and the ability to make causal claims. However, these capabilities require information external to that data themselves, and too often that information makes implausible demands on how nature is supposed to function. Convenience samples are treated as if they are random samples. Causal status is given to predictors that cannot be manipulated. Disturbance terms are assumed to behave not as nature might produce them, but as required by the model. Regression Analysis: A Constructive Critique identifies a wide variety of problems with regression analysis as it is commonly used and then provides a number of ways in which practice could be improved. Regression is most useful for data reduction, leading to relatively simple but rich and precise descriptions of patterns in a data set. The emphasis on description provides readers with an insightful rethinking from the ground up of what regression analysis can do, so that readers can better match regression analysis with useful empirical questions and improved policy-related research. "An interesting and lively text, rich in practical wisdom, written for people who do empirical work in the social sciences and their graduate students." --David A. Freedman, Professor of Statistics, University of California, Berkeley



Statistical Inference As Severe Testing


Statistical Inference As Severe Testing
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Author : Deborah G. Mayo
language : en
Publisher: Cambridge University Press
Release Date : 2018-09-20

Statistical Inference As Severe Testing written by Deborah G. Mayo and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-20 with Mathematics categories.


Unlock today's statistical controversies and irreproducible results by viewing statistics as probing and controlling errors.



Principles Of Statistical Inference


Principles Of Statistical Inference
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Author : D. R. Cox
language : en
Publisher: Cambridge University Press
Release Date : 2006-08-10

Principles Of Statistical Inference written by D. R. Cox and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-08-10 with Mathematics categories.


In this definitive book, D. R. Cox gives a comprehensive and balanced appraisal of statistical inference. He develops the key concepts, describing and comparing the main ideas and controversies over foundational issues that have been keenly argued for more than two-hundred years. Continuing a sixty-year career of major contributions to statistical thought, no one is better placed to give this much-needed account of the field. An appendix gives a more personal assessment of the merits of different ideas. The content ranges from the traditional to the contemporary. While specific applications are not treated, the book is strongly motivated by applications across the sciences and associated technologies. The mathematics is kept as elementary as feasible, though previous knowledge of statistics is assumed. The book will be valued by every user or student of statistics who is serious about understanding the uncertainty inherent in conclusions from statistical analyses.



Statistical Methods In Diagnostic Medicine


Statistical Methods In Diagnostic Medicine
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Author : Xiao-Hua Zhou
language : en
Publisher: John Wiley & Sons
Release Date : 2014-08-21

Statistical Methods In Diagnostic Medicine written by Xiao-Hua Zhou and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-08-21 with Medical categories.


Praise for the First Edition " . . . the book is a valuable addition to the literature in the field, serving as a much-needed guide for both clinicians and advanced students."—Zentralblatt MATH A new edition of the cutting-edge guide to diagnostic tests in medical research In recent years, a considerable amount of research has focused on evolving methods for designing and analyzing diagnostic accuracy studies. Statistical Methods in Diagnostic Medicine, Second Edition continues to provide a comprehensive approach to the topic, guiding readers through the necessary practices for understanding these studies and generalizing the results to patient populations. Following a basic introduction to measuring test accuracy and study design, the authors successfully define various measures of diagnostic accuracy, describe strategies for designing diagnostic accuracy studies, and present key statistical methods for estimating and comparing test accuracy. Topics new to the Second Edition include: Methods for tests designed to detect and locate lesions Recommendations for covariate-adjustment Methods for estimating and comparing predictive values and sample size calculations Correcting techniques for verification and imperfect standard biases Sample size calculation for multiple reader studies when pilot data are available Updated meta-analysis methods, now incorporating random effects Three case studies thoroughly showcase some of the questions and statistical issues that arise in diagnostic medicine, with all associated data provided in detailed appendices. A related web site features Fortran, SAS®, and R software packages so that readers can conduct their own analyses. Statistical Methods in Diagnostic Medicine, Second Edition is an excellent supplement for biostatistics courses at the graduate level. It also serves as a valuable reference for clinicians and researchers working in the fields of medicine, epidemiology, and biostatistics.