Download or read book James Stein Rule Estimators in Linear Regression Models with Errors Having Multivariate Student t Distribution written by Radhey S. Singh and published by Guelph, Ont. : Department of Mathematics and Statistics, University of Guelph. This book was released on 1989 with total page 46 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Handbook Of Applied Econometrics And Statistical Inference written by Aman Ullah and published by CRC Press. This book was released on 2002-01-29 with total page 741 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summarizes developments and techniques in the field. It highlights areas such as sample surveys, nonparametic analysis, hypothesis testing, time series analysis, Bayesian inference, and distribution theory for applications in statistics, economics, medicine, biology, and engineering.
Download or read book Statistical Inference for Models with Multivariate t Distributed Errors written by A. K. Md. Ehsanes Saleh and published by John Wiley & Sons. This book was released on 2014-10-01 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book summarizes the results of various models under normal theory with a brief review of the literature. Statistical Inference for Models with Multivariate t-Distributed Errors: Includes a wide array of applications for the analysis of multivariate observations Emphasizes the development of linear statistical models with applications to engineering, the physical sciences, and mathematics Contains an up-to-date bibliography featuring the latest trends and advances in the field to provide a collective source for research on the topic Addresses linear regression models with non-normal errors with practical real-world examples Uniquely addresses regression models in Student's t-distributed errors and t-models Supplemented with an Instructor's Solutions Manual, which is available via written request by the Publisher
Download or read book A Family of Improved Estimators in Linear Regression Models with Errors Having Multivariate Student t Distribution written by Radhey S. Singh and published by . This book was released on 1987 with total page 23 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book The Statistical Implications of Pre test and Stein rule Estimators in Econometrics written by George G. Judge and published by North-Holland. This book was released on 1978 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Pitman Nearness Criterion of the Stein rule Estimator in a Linear Regression Model with Omitted Relevant Regressors and Multivariate T Errors written by Akio Namba and published by . This book was released on 2002 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Improving Efficiency by Shrinkage written by Marvin Gruber and published by Routledge. This book was released on 2017-11-01 with total page 648 pages. Available in PDF, EPUB and Kindle. Book excerpt: Offers a treatment of different kinds of James-Stein and ridge regression estimators from a frequentist and Bayesian point of view. The book explains and compares estimators analytically as well as numerically and includes Mathematica and Maple programs used in numerical comparison.;College or university bookshops may order five or more copies at a special student rate, available on request.
Download or read book Statistical Theory and Method Abstracts written by and published by . This book was released on 1992 with total page 510 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Empirical Bayes James Stein and Ridge Regression Type Estimators for Linear Models written by Marvin Haskel Jack Gruber and published by . This book was released on 1979 with total page 720 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book A Note on Estimating the Improvement in Stein type Estimators written by STANFORD UNIV CA DEPT OF STATISTICS. and published by . This book was released on 1986 with total page 15 pages. Available in PDF, EPUB and Kindle. Book excerpt: In estimating the mean of a multivariate normal distribution under squared error loss, the maximum likelihood estimation may be uniformly improved upon. For a dominating estimator, how may we estimate the improvement? Results from Efron and Morris enable a unique unbiased estimator of the improvement for orthogonally invariant estimators. However, this estimator needn't be admissible. We develop, for the James-Stein estimator, alternative improvement estimators. (Author).
Download or read book Journal of the American Statistical Association written by and published by . This book was released on 2004 with total page 1298 pages. Available in PDF, EPUB and Kindle. Book excerpt: A scientific and educational journal not only for professional statisticians but also for economists, business executives, research directors, government officials, university professors, and others who are seriously interested in the application of statistical methods to practical problems, in the development of more useful methods, and in the improvement of basic statistical data.
Download or read book The Bayesian Choice written by Christian P. Robert and published by Springer Science & Business Media. This book was released on 2013-04-17 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: This graduate-level textbook covers both the basic ideas of statistical theory, and also some of the more modern and advanced topics of Bayesian statistics, such as complete class theorems, the Stein effect, hierarchical and empirical Bayes modelling, Monte Carlo integration, and Gibbs sampling. In translating the book from the original French, the author has taken the opportunity to add and update material, and to include many problems and exercises for students.
Download or read book Unbiased Estimators and their Applications written by V.G. Voinov and published by Springer Science & Business Media. This book was released on 1996-01-31 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume is a continuation of Unbiased Estimators and Their Applications, Vol. I: Univariate Case. It contains problems of parametric point estimation for multivariate probability distributions emphasizing problems of unbiased estimation. The volume consists of four chapters dealing, respectively, with some basic properties of multivariate continuous and discrete distributions, the general theory of point estimation in multivariate case, techniques for constructing unbiased estimators and applications of unbiased estimation theory in the multivariate case. These chapters contain numerous examples, many applications and are followed by a comprehensive Appendix which classifies and lists, in the form of tables, all known results relating to unbiased estimators of parameter functions for multivariate distributions. Audience: This volume will serve as a handbook on point unbiased estimation for researchers whose work involves statistics. It can also be recommended as a supplementary text for undergraduate and graduate students.
Download or read book Multivariate T Distributions and Their Applications written by Samuel Kotz and published by Cambridge University Press. This book was released on 2004-02-16 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Almost all the results available in the literature on multivariate t-distributions published in the last 50 years are now collected together in this comprehensive reference. Because these distributions are becoming more prominent in many applications, this book is a must for any serious researcher or consultant working in multivariate analysis and statistical distributions. Much of this material has never before appeared in book form. The first part of the book emphasizes theoretical results of a probabilistic nature. In the second part of the book, these are supplemented by a variety of statistical aspects. Various generalizations and applications are dealt with in the final chapters. The material on estimation and regression models is of special value for practitioners in statistics and economics. A comprehensive bibliography of over 350 references is included.
Download or read book Stein Estimation written by Yuzo Maruyama and published by Springer. This book was released on 2023-10-25 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a self-contained introduction of Stein/shrinkage estimation for the mean vector of a multivariate normal distribution. The book begins with a brief discussion of basic notions and results from decision theory such as admissibility, minimaxity, and (generalized) Bayes estimation. It also presents Stein's unbiased risk estimator and the James-Stein estimator in the first chapter. In the following chapters, the authors consider estimation of the mean vector of a multivariate normal distribution in the known and unknown scale case when the covariance matrix is a multiple of the identity matrix and the loss is scaled squared error. The focus is on admissibility, inadmissibility, and minimaxity of (generalized) Bayes estimators, where particular attention is paid to the class of (generalized) Bayes estimators with respect to an extended Strawderman-type prior. For almost all results of this book, the authors present a self-contained proof. The book is helpful for researchers and graduate students in various fields requiring data analysis skills as well as in mathematical statistics.
Download or read book Probability and Statistics for Economists written by Bruce Hansen and published by Princeton University Press. This book was released on 2022-06-28 with total page 417 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive and up-to-date introduction to the mathematics that all economics students need to know Probability theory is the quantitative language used to handle uncertainty and is the foundation of modern statistics. Probability and Statistics for Economists provides graduate and PhD students with an essential introduction to mathematical probability and statistical theory, which are the basis of the methods used in econometrics. This incisive textbook teaches fundamental concepts, emphasizes modern, real-world applications, and gives students an intuitive understanding of the mathematics that every economist needs to know. Covers probability and statistics with mathematical rigor while emphasizing intuitive explanations that are accessible to economics students of all backgrounds Discusses random variables, parametric and multivariate distributions, sampling, the law of large numbers, central limit theory, maximum likelihood estimation, numerical optimization, hypothesis testing, and more Features hundreds of exercises that enable students to learn by doing Includes an in-depth appendix summarizing important mathematical results as well as a wealth of real-world examples Can serve as a core textbook for a first-semester PhD course in econometrics and as a companion book to Bruce E. Hansen’s Econometrics Also an invaluable reference for researchers and practitioners
