Regression Estimators
Download Regression Estimators PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Regression Estimators book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Indirect Estimators in U.S. Federal Programs
Author: Wesley L. Schaible
language: en
Publisher: Springer Science & Business Media
Release Date: 2013-11-11
In 1991, a subcommittee of the Federal Committee on Statistical Methodology met to document the use of indirect estimators - that is, estimators which use data drawn from a domain or time different from the domain or time for which an estimate is required. This volume comprises the eight reports which describe the use of indirect estimators and they are based on case studies from a variety of federal programs. As a result, many researchers will find this book provides a valuable survey of how indirect estimators are used in practice and which addresses some of the pitfalls of these methods.
Introduction to Robust Estimation and Hypothesis Testing
"This book focuses on the practical aspects of modern and robust statistical methods. The increased accuracy and power of modern methods, versus conventional approaches to the analysis of variance (ANOVA) and regression, is remarkable. Through a combination of theoretical developments, improved and more flexible statistical methods, and the power of the computer, it is now possible to address problems with standard methods that seemed insurmountable only a few years ago"--
Theory of Preliminary Test and Stein-Type Estimation with Applications
Author: A. K. Md. Ehsanes Saleh
language: en
Publisher: John Wiley & Sons
Release Date: 2006-04-28
Theory of Preliminary Test and Stein-Type Estimation with Applications provides a com-prehensive account of the theory and methods of estimation in a variety of standard models used in applied statistical inference. It is an in-depth introduction to the estimation theory for graduate students, practitioners, and researchers in various fields, such as statistics, engineering, social sciences, and medical sciences. Coverage of the material is designed as a first step in improving the estimates before applying full Bayesian methodology, while problems at the end of each chapter enlarge the scope of the applications. This book contains clear and detailed coverage of basic terminology related to various topics, including: * Simple linear model; ANOVA; parallelism model; multiple regression model with non-stochastic and stochastic constraints; regression with autocorrelated errors; ridge regression; and multivariate and discrete data models * Normal, non-normal, and nonparametric theory of estimation * Bayes and empirical Bayes methods * R-estimation and U-statistics * Confidence set estimation