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Approximate Minimax Estimators In The Linear Regression Model
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Book Synopsis Approximate minimax estimators in the linear regression model by : Peter Stahlecker
Download or read book Approximate minimax estimators in the linear regression model written by Peter Stahlecker and published by . This book was released on 1986 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A numerical method for an approximate minimax estimator in linear regression by : Jörg Lauterbach
Download or read book A numerical method for an approximate minimax estimator in linear regression written by Jörg Lauterbach and published by . This book was released on 1990 with total page 58 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Approximate linear minimax estimation in regression analysis with ellipsoidal constraints by : Peter Stahlecker
Download or read book Approximate linear minimax estimation in regression analysis with ellipsoidal constraints written by Peter Stahlecker and published by . This book was released on 1987 with total page 27 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Minimax Estimation in Regression and Random Censorship Models by : Eduard N. Belitser
Download or read book Minimax Estimation in Regression and Random Censorship Models written by Eduard N. Belitser and published by . This book was released on 2000 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Regression Estimators by : Marvin H. J. Gruber
Download or read book Regression Estimators written by Marvin H. J. Gruber and published by Academic Press. This book was released on 2014-05-10 with total page 361 pages. Available in PDF, EPUB and Kindle. Book excerpt: Regression Estimators: A Comparative Study presents, compares, and contrasts the development and the properties of the ridge type estimators that result from both Bayesian and non-Bayesian (frequentist) methods. The book is divided into four parts. The first part (Chapters I and II) discusses the need for alternatives to least square estimators, gives a historical survey of the literature and summarizes basic ideas in Matrix Theory and Statistical Decision Theory used throughout the book. The second part (Chapters III and IV) covers the estimators from both the Bayesian and from the frequentist points of view and explores the mathematical relationships between them. The third part (Chapters V-VIII) considers the efficiency of the estimators with and without averaging over a prior distribution. Part IV, the final two chapters IX and X, suggests applications of the methods and results of Chapters III-VII to Kaiman Filters and Analysis of Variance, two very important areas of application. Statisticians and workers in fields that use statistical methods who would like to know more about the analytical properties of ridge type estimators will find the book invaluable.
Book Synopsis Approximative Minimax Estimators in the Linear Regression Model by : Peter Stahlecker
Download or read book Approximative Minimax Estimators in the Linear Regression Model written by Peter Stahlecker and published by . This book was released on 1985 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Approximative minimax estimators in the linear regression model by : Jörg Lauterbach
Download or read book Approximative minimax estimators in the linear regression model written by Jörg Lauterbach and published by . This book was released on 1985 with total page 15 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 Approximative minimax estimators in the linear regression model by : Jörg Lauterbach
Download or read book Approximative minimax estimators in the linear regression model written by Jörg Lauterbach and published by . This book was released on 1985 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Improving Efficiency by Shrinkage by : Marvin Gruber
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 664 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.
Book Synopsis Approximate Confidence Regions for Minimax-linear Estimators by : Helge Toutenburg
Download or read book Approximate Confidence Regions for Minimax-linear Estimators written by Helge Toutenburg and published by . This book was released on 1999 with total page 19 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Journal of Statistical Planning and Inference by :
Download or read book Journal of Statistical Planning and Inference written by and published by . This book was released on 1993 with total page 886 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Quasi minimax estimation in the linear regression model by : Peter Stahlecker
Download or read book Quasi minimax estimation in the linear regression model written by Peter Stahlecker and published by . This book was released on 1984 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Minimax Estimation in Linear Regression Under Restrictions by : Helge Blaker
Download or read book Minimax Estimation in Linear Regression Under Restrictions written by Helge Blaker and published by . This book was released on 1998 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bayesian Estimation and Experimental Design in Linear Regression Models by : Jürgen Pilz
Download or read book Bayesian Estimation and Experimental Design in Linear Regression Models written by Jürgen Pilz and published by . This book was released on 1991-07-09 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents a clear treatment of the design and analysis of linear regression experiments in the presence of prior knowledge about the model parameters. Develops a unified approach to estimation and design; provides a Bayesian alternative to the least squares estimator; and indicates methods for the construction of optimal designs for the Bayes estimator. Material is also applicable to some well-known estimators using prior knowledge that is not available in the form of a prior distribution for the model parameters; such as mixed linear, minimax linear and ridge-type estimators.
Book Synopsis Approximate minimax estimators for a bounded normal mean by : Jürgen Eichenauer-Herrmann
Download or read book Approximate minimax estimators for a bounded normal mean written by Jürgen Eichenauer-Herrmann and published by . This book was released on 1987 with total page 17 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Minimax Ridge Regression Estimation by : George Casella
Download or read book Minimax Ridge Regression Estimation written by George Casella and published by . This book was released on 1977 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: