Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Statistical Inference For Parameters Subject To Order Restrictions
Download Statistical Inference For Parameters Subject To Order Restrictions full books in PDF, epub, and Kindle. Read online Statistical Inference For Parameters Subject To Order Restrictions ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Book Synopsis Advances in Order Restricted Statistical Inference by : Richard Dykstra
Download or read book Advances in Order Restricted Statistical Inference written by Richard Dykstra and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 305 pages. Available in PDF, EPUB and Kindle. Book excerpt: With support from the University of Iowa and the Office of Naval Research. a small conference on order restricted inference was held at the University of Iowa in Iowa City in April of 1981. There were twenty-one participants. mostly from the midwest. and eleven talks were presented. A highlight of the conference was a talk by D. J. Bartholomew on. "Reflections on the past and thoughts about the future. " The conference was especially valuable because it brought together researchers who were thinking about related problems. A small conference on a limited topic is one of the best ways to stimulate research and facilitate collaboration. Because of the success of the first conference. a second conference was organized and held in September of 1985. This second conference was made possible again by support from the Office of Naval Research under Department of the Navy Contract NOOOI4-85-0161 and the University of Iowa. There were thirty-five participants and twenty presentations on a wide variety of topics dealing with order restricted inference at the second conference. This volume is a collection of fourteen of those presentations. By collecting together and organizing the fundamental results in order restricted inference in Statistical Inference under Order Restrictions. R. E. Barlow. D. J. Bartholomew. J. M. Bremner and H. D. Brunk have done much to stimulate research in this area. and so we wish to express our gratitude to them first.
Book Synopsis Statistical Inference Under Order Restrictions by :
Download or read book Statistical Inference Under Order Restrictions written by and published by . This book was released on 1972 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: ;Contents: Isotonic regression; Estimation under order restrictions; Testing the equality of ordered means--likelihood ratio tests in the normal case; Testing the equality of ordered means--extensions and generalizations; Estimation of distributions; Isotonic tests for goodness of fit; Conditional expectation given a sigma-lattice.
Book Synopsis Constrained Statistical Inference by : Mervyn J. Silvapulle
Download or read book Constrained Statistical Inference written by Mervyn J. Silvapulle and published by John Wiley & Sons. This book was released on 2011-09-15 with total page 560 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Statistical Inference Based on Divergence Measures by : Leandro Pardo
Download or read book Statistical Inference Based on Divergence Measures written by Leandro Pardo and published by CRC Press. This book was released on 2018-11-12 with total page 513 pages. Available in PDF, EPUB and Kindle. Book excerpt: The idea of using functionals of Information Theory, such as entropies or divergences, in statistical inference is not new. However, in spite of the fact that divergence statistics have become a very good alternative to the classical likelihood ratio test and the Pearson-type statistic in discrete models, many statisticians remain unaware of this p
Book Synopsis Statistical Inference as Severe Testing by : Deborah G. Mayo
Download or read book Statistical Inference as Severe Testing written by Deborah G. Mayo and published by Cambridge University Press. This book was released on 2018-09-20 with total page 503 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mounting failures of replication in social and biological sciences give a new urgency to critically appraising proposed reforms. This book pulls back the cover on disagreements between experts charged with restoring integrity to science. It denies two pervasive views of the role of probability in inference: to assign degrees of belief, and to control error rates in a long run. If statistical consumers are unaware of assumptions behind rival evidence reforms, they can't scrutinize the consequences that affect them (in personalized medicine, psychology, etc.). The book sets sail with a simple tool: if little has been done to rule out flaws in inferring a claim, then it has not passed a severe test. Many methods advocated by data experts do not stand up to severe scrutiny and are in tension with successful strategies for blocking or accounting for cherry picking and selective reporting. Through a series of excursions and exhibits, the philosophy and history of inductive inference come alive. Philosophical tools are put to work to solve problems about science and pseudoscience, induction and falsification.
Book Synopsis Essential Statistical Inference by : Dennis D. Boos
Download or read book Essential Statistical Inference written by Dennis D. Boos and published by Springer Science & Business Media. This book was released on 2013-02-06 with total page 567 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is for students and researchers who have had a first year graduate level mathematical statistics course. It covers classical likelihood, Bayesian, and permutation inference; an introduction to basic asymptotic distribution theory; and modern topics like M-estimation, the jackknife, and the bootstrap. R code is woven throughout the text, and there are a large number of examples and problems. An important goal has been to make the topics accessible to a wide audience, with little overt reliance on measure theory. A typical semester course consists of Chapters 1-6 (likelihood-based estimation and testing, Bayesian inference, basic asymptotic results) plus selections from M-estimation and related testing and resampling methodology. Dennis Boos and Len Stefanski are professors in the Department of Statistics at North Carolina State. Their research has been eclectic, often with a robustness angle, although Stefanski is also known for research concentrated on measurement error, including a co-authored book on non-linear measurement error models. In recent years the authors have jointly worked on variable selection methods.
Book Synopsis Multiple Decision Procedures by : Shanti S. Gupta
Download or read book Multiple Decision Procedures written by Shanti S. Gupta and published by SIAM. This book was released on 2002-01-01 with total page 592 pages. Available in PDF, EPUB and Kindle. Book excerpt: An encyclopaedic coverage of the literature in the area of ranking and selection procedures. It also deals with the estimation of unknown ordered parameters. This book can serve as a text for a graduate topics course in ranking and selection. It is also a valuable reference for researchers and practitioners.
Book Synopsis Order Restricted Statistical Inference by : Tim Robertson
Download or read book Order Restricted Statistical Inference written by Tim Robertson and published by John Wiley & Sons Incorporated. This book was released on 1988 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work attempts to provide a comprehensive treatment of the topic of statistical inference under inequality constraints, in which much of the theory is based on the principles ofr maximum likelihood estimation and likelihood ratio tests.
Book Synopsis Bayesian Evaluation of Informative Hypotheses by : Herbert Hoijtink
Download or read book Bayesian Evaluation of Informative Hypotheses written by Herbert Hoijtink and published by Springer Science & Business Media. This book was released on 2008-09-08 with total page 361 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an overview of the developments in the area of Bayesian evaluation of informative hypotheses that took place since the publication of the ?rst paper on this topic in 2001 [Hoijtink, H. Con?rmatory latent class analysis, model selection using Bayes factors and (pseudo) likelihood ratio statistics. Multivariate Behavioral Research, 36, 563–588]. The current state of a?airs was presented and discussed by the authors of this book during a workshop in Utrecht in June 2007. Here we would like to thank all authors for their participation, ideas, and contributions. We would also like to thank Sophie van der Zee for her editorial e?orts during the construction of this book. Another word of thanks is due to John Kimmel of Springer for his con?dence in the editors and authors. Finally, we would like to thank the Netherlands Organization for Scienti?c Research (NWO) whose VICI grant (453-05-002) awarded to the ?rst author enabled the organization of the workshop, the writing of this book, and continuation of the research with respect to Bayesian evaluation of informative hypotheses.
Book Synopsis Restricted Parameter Space Estimation Problems by : Constance van Eeden
Download or read book Restricted Parameter Space Estimation Problems written by Constance van Eeden and published by Springer Science & Business Media. This book was released on 2006-12-15 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph is addressed to anyone interested in the subject of restrict- parameter-space estimation, and in particular to those who want to learn, or bring their knowledge up to date, about (in)admissibility and minimaxity problems for such parameter spaces. The coverage starts in the early 1950s when the subject of inference for - stricted parameter spaces began to be studied and ends around the middle of 2004. It presents known, and also some new, results on (in)admissibility and minimaxity for nonsequential point estimation problems in restricted ?ni- dimensional parameter spaces. Relationships between various results are d- cussed and open problems are pointed out. Few complete proofs are given, but outlines of proofs are often supplied. The reader is always referred to the published papers and often results are clari?ed by presenting examples of the kind of problems an author solves, or of problems that cannot be solved by a particular result. The monograph does not touch on the subject of testing hypotheses in - stricted parameter spaces. The latest books on that subject are by Robertson, Wright and Dykstra (1988) and Akkerboom (1990), but many new results in that area have been obtained since. The monograph does have a chapter in which questions about the existence of maximum likelihood estimators are discussed. Some of their properties are also given there as well as some algorithms for computing them. Most of these results cannot be found in the Robertson, Wright, Dykstra book.
Book Synopsis All of Statistics by : Larry Wasserman
Download or read book All of Statistics written by Larry Wasserman and published by Springer Science & Business Media. This book was released on 2013-12-11 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.
Book Synopsis Modern Directional Statistics by : Christophe Ley
Download or read book Modern Directional Statistics written by Christophe Ley and published by CRC Press. This book was released on 2017-08-03 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern Directional Statistics collects important advances in methodology and theory for directional statistics over the last two decades. It provides a detailed overview and analysis of recent results that can help both researchers and practitioners. Knowledge of multivariate statistics eases the reading but is not mandatory. The field of directional statistics has received a lot of attention over the past two decades, due to new demands from domains such as life sciences or machine learning, to the availability of massive data sets requiring adapted statistical techniques, and to technological advances. This book covers important progresses in distribution theory,high-dimensional statistics, kernel density estimation, efficient inference on directional supports, and computational and graphical methods. Christophe Ley is professor of mathematical statistics at Ghent University. His research interests include semi-parametrically efficient inference, flexible modeling, directional statistics and the study of asymptotic approximations via Stein’s Method. His achievements include the Marie-Jeanne Laurent-Duhamel prize of the Société Française de Statistique and an elected membership at the International Statistical Institute. He is associate editor for the journals Computational Statistics & Data Analysis and Econometrics and Statistics. Thomas Verdebout is professor of mathematical statistics at Université libre de Bruxelles (ULB). His main research interests are semi-parametric statistics, high- dimensional statistics, directional statistics and rank-based procedures. He has won an annual prize of the Belgian Academy of Sciences and is an elected member of the International Statistical Institute. He is associate editor for the journals Statistics and Probability Letters and Journal of Multivariate Analysis.
Book Synopsis Topics in Statistical Dependence by : Henry W. Block
Download or read book Topics in Statistical Dependence written by Henry W. Block and published by IMS. This book was released on 1990 with total page 558 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Geometry Driven Statistics by : Ian L. Dryden
Download or read book Geometry Driven Statistics written by Ian L. Dryden and published by John Wiley & Sons. This book was released on 2015-09-03 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: A timely collection of advanced, original material in the area of statistical methodology motivated by geometric problems, dedicated to the influential work of Kanti V. Mardia This volume celebrates Kanti V. Mardia's long and influential career in statistics. A common theme unifying much of Mardia’s work is the importance of geometry in statistics, and to highlight the areas emphasized in his research this book brings together 16 contributions from high-profile researchers in the field. Geometry Driven Statistics covers a wide range of application areas including directional data, shape analysis, spatial data, climate science, fingerprints, image analysis, computer vision and bioinformatics. The book will appeal to statisticians and others with an interest in data motivated by geometric considerations. Summarizing the state of the art, examining some new developments and presenting a vision for the future, Geometry Driven Statistics will enable the reader to broaden knowledge of important research areas in statistics and gain a new appreciation of the work and influence of Kanti V. Mardia.
Book Synopsis Statistical Inference for Discrete Time Stochastic Processes by : M. B. Rajarshi
Download or read book Statistical Inference for Discrete Time Stochastic Processes written by M. B. Rajarshi and published by Springer Science & Business Media. This book was released on 2014-07-08 with total page 121 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work is an overview of statistical inference in stationary, discrete time stochastic processes. Results in the last fifteen years, particularly on non-Gaussian sequences and semi-parametric and non-parametric analysis have been reviewed. The first chapter gives a background of results on martingales and strong mixing sequences, which enable us to generate various classes of CAN estimators in the case of dependent observations. Topics discussed include inference in Markov chains and extension of Markov chains such as Raftery's Mixture Transition Density model and Hidden Markov chains and extensions of ARMA models with a Binomial, Poisson, Geometric, Exponential, Gamma, Weibull, Lognormal, Inverse Gaussian and Cauchy as stationary distributions. It further discusses applications of semi-parametric methods of estimation such as conditional least squares and estimating functions in stochastic models. Construction of confidence intervals based on estimating functions is discussed in some detail. Kernel based estimation of joint density and conditional expectation are also discussed. Bootstrap and other resampling procedures for dependent sequences such as Markov chains, Markov sequences, linear auto-regressive moving average sequences, block based bootstrap for stationary sequences and other block based procedures are also discussed in some detail. This work can be useful for researchers interested in knowing developments in inference in discrete time stochastic processes. It can be used as a material for advanced level research students.
Book Synopsis Principles of Statistical Inference by : D. R. Cox
Download or read book Principles of Statistical Inference written by D. R. Cox and published by Cambridge University Press. This book was released on 2006-08-10 with total page 227 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Introduction to Statistical Limit Theory by : Alan M. Polansky
Download or read book Introduction to Statistical Limit Theory written by Alan M. Polansky and published by CRC Press. This book was released on 2011-01-07 with total page 353 pages. Available in PDF, EPUB and Kindle. Book excerpt: Helping students develop a good understanding of asymptotic theory, Introduction to Statistical Limit Theory provides a thorough yet accessible treatment of common modes of convergence and their related tools used in statistics. It also discusses how the results can be applied to several common areas in the field.The author explains as much of the
