Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Download Mean Squared Error Of Estimation And Prediction Under A General Linear Model full books in PDF, epub, and Kindle. Read online Mean Squared Error Of Estimation And Prediction Under A General Linear Model ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Author : D. A. Harville
Publisher :
ISBN 13 :
Total Pages : 24 pages
Book Rating : 4.:/5 (897 download)
Download or read book Mean Squared Error of Estimation Or Prediction Under a General Linear Model written by D. A. Harville and published by . This book was released on 1989 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : D. R. Jeske
Publisher :
ISBN 13 :
Total Pages : 12 pages
Book Rating : 4.:/5 (897 download)
Download or read book Mean Squared Error of Estimation and Prediction Under a General Linear Model written by D. R. Jeske and published by . This book was released on 1987 with total page 12 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Lyu Ziyang
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (144 download)
Download or read book Estimation of Prediction Mean Squared Error in Linear Mixed Models written by Lyu Ziyang and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Best linear unbiased estimators (BLUEs) theta and predictors (BLUPs) of the fixed and random effects of mixed linear models are available when the true values of the variance components are known. When the true values theta are replaced by estimated values to produce estimated best linear unbiased predictors (EBLUPs), the mean squared errors (MSE) of the predictors of random effects increase in size. Henderson (1975) suggested using the naive estimator of the MSE of the EBLUP obtained by replacing theta by hat of theta in the expression of the MSE of the BLUP. However, this is an underestimate. In this thesis we will study three ways to estimate the MSE of the EBLUP. We describe how Kackar and Harville (1984) obtained an estimator to the MSE of EBLUP under the linear mixed model. We also study another estimator, which Prasad and Rao (1990) proposed, based on Kackar and Harville (1984). We apply both methods to two sample models and to see how they differ. We also describe Jiang et al. (2002) jackknife estimator and Lohr and Rao (2009) modified jackknife estimator. We report the results of a small simulation study to compare the different estimators.
Author : Kevin Kim
Publisher : CRC Press
ISBN 13 : 1000738256
Total Pages : 576 pages
Book Rating : 4.0/5 (7 download)
Download or read book Univariate and Multivariate General Linear Models written by Kevin Kim and published by CRC Press. This book was released on 2006-10-11 with total page 576 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reviewing the theory of the general linear model (GLM) using a general framework, Univariate and Multivariate General Linear Models: Theory and Applications with SAS, Second Edition presents analyses of simple and complex models, both univariate and multivariate, that employ data sets from a variety of disciplines, such as the social and behavioral
Author : Peter Bruce
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491952911
Total Pages : 322 pages
Book Rating : 4.4/5 (919 download)
Download or read book Practical Statistics for Data Scientists written by Peter Bruce and published by "O'Reilly Media, Inc.". This book was released on 2017-05-10 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data
Author : Douglas C. Montgomery
Publisher : Wiley-Interscience
ISBN 13 :
Total Pages : 680 pages
Book Rating : 4.F/5 ( download)
Download or read book Introduction to Linear Regression Analysis written by Douglas C. Montgomery and published by Wiley-Interscience. This book was released on 2001-04-16 with total page 680 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive and thoroughly up-to-date look at regression analysis-still the most widely used technique in statistics today As basic to statistics as the Pythagorean theorem is to geometry, regression analysis is a statistical technique for investigating and modeling the relationship between variables. With far-reaching applications in almost every field, regression analysis is used in engineering, the physical and chemical sciences, economics, management, life and biological sciences, and the social sciences. Clearly balancing theory with applications, Introduction to Linear Regression Analysis describes conventional uses of the technique, as well as less common ones, placing linear regression in the practical context of today's mathematical and scientific research. Beginning with a general introduction to regression modeling, including typical applications, the book then outlines a host of technical tools that form the linear regression analytical arsenal, including: basic inference procedures and introductory aspects of model adequacy checking; how transformations and weighted least squares can be used to resolve problems of model inadequacy; how to deal with influential observations; and polynomial regression models and their variations. Succeeding chapters include detailed coverage of: ? Indicator variables, making the connection between regression and analysis-of-variance modelss ? Variable selection and model-building techniques ? The multicollinearity problem, including its sources, harmful effects, diagnostics, and remedial measures ? Robust regression techniques, including M-estimators, Least Median of Squares, and S-estimation ? Generalized linear models The book also includes material on regression models with autocorrelated errors, bootstrapping regression estimates, classification and regression trees, and regression model validation. Topics not usually found in a linear regression textbook, such as nonlinear regression and generalized linear models, yet critical to engineering students and professionals, have also been included. The new critical role of the computer in regression analysis is reflected in the book's expanded discussion of regression diagnostics, where major analytical procedures now available in contemporary software packages, such as SAS, Minitab, and S-Plus, are detailed. The Appendix now includes ample background material on the theory of linear models underlying regression analysis. Data sets from the book, extensive problem solutions, and software hints are available on the ftp site. For other Wiley books by Doug Montgomery, visit our website at www.wiley.com/college/montgomery.
Author : Walter W. Stroup
Publisher : CRC Press
ISBN 13 : 1498755585
Total Pages : 671 pages
Book Rating : 4.4/5 (987 download)
Download or read book Generalized Linear Mixed Models written by Walter W. Stroup and published by CRC Press. This book was released on 2024-05-21 with total page 671 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generalized Linear Mixed Models: Modern Concepts, Methods, and Applications (2nd edition) presents an updated introduction to linear modeling using the generalized linear mixed model (GLMM) as the overarching conceptual framework. For students new to statistical modeling, this book helps them see the big picture – linear modeling as broadly understood and its intimate connection with statistical design and mathematical statistics. For readers experienced in statistical practice, but new to GLMMs, the book provides a comprehensive introduction to GLMM methodology and its underlying theory. Unlike textbooks that focus on classical linear models or generalized linear models or mixed models, this book covers all of the above as members of a unified GLMM family of linear models. In addition to essential theory and methodology, this book features a rich collection of examples using SAS® software to illustrate GLMM practice. This second edition is updated to reflect lessons learned and experience gained regarding best practices and modeling choices faced by GLMM practitioners. New to this edition are two chapters focusing on Bayesian methods for GLMMs. Key Features: • Most statistical modeling books cover classical linear models or advanced generalized and mixed models; this book covers all members of the GLMM family – classical and advanced models. • Incorporates lessons learned from experience and on-going research to provide up-to-date examples of best practices. • Illustrates connections between statistical design and modeling: guidelines for translating study design into appropriate model and in-depth illustrations of how to implement these guidelines; use of GLMM methods to improve planning and design. • Discusses the difference between marginal and conditional models, differences in the inference space they are intended to address and when each type of model is appropriate. • In addition to likelihood-based frequentist estimation and inference, provides a brief introduction to Bayesian methods for GLMMs. Walt Stroup is an Emeritus Professor of Statistics. He served on the University of Nebraska statistics faculty for over 40 years, specializing in statistical modeling and statistical design. He is a Fellow of the American Statistical Association, winner of the University of Nebraska Outstanding Teaching and Innovative Curriculum Award and author or co-author of three books on mixed models and their extensions. Marina Ptukhina (Pa-too-he-nuh), PhD, is an Associate Professor of Statistics at Whitman College. She is interested in statistical modeling, design and analysis of research studies and their applications. Her research includes applications of statistics to economics, biostatistics and statistical education. Ptukhina earned a PhD in Statistics from the University of Nebraska-Lincoln, a Master of Science degree in Mathematics from Texas Tech University and a Specialist degree in Management from The National Technical University "Kharkiv Polytechnic Institute." Julie Garai, PhD, is a Data Scientist at Loop. She earned her PhD in Statistics from the University of Nebraska-Lincoln and a bachelor’s degree in Mathematics and Spanish from Doane College. Dr Garai actively collaborates with statisticians, psychologists, ecologists, forest scientists, software engineers, and business leaders in academia and industry. In her spare time, she enjoys leisurely walks with her dogs, dance parties with her children, and playing the trombone.
Author : Frantisek Stulajter
Publisher : Springer Science & Business Media
ISBN 13 : 1475736290
Total Pages : 237 pages
Book Rating : 4.4/5 (757 download)
Download or read book Predictions in Time Series Using Regression Models written by Frantisek Stulajter and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 237 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book will interest and assist people who are dealing with the problems of predictions of time series in higher education and research. It will greatly assist people who apply time series theory to practical problems in their work and also serve as a textbook for postgraduate students in statistics economics and related subjects.
Author : Shonosuke Sugasawa
Publisher : Springer Nature
ISBN 13 : 9811994862
Total Pages : 127 pages
Book Rating : 4.8/5 (119 download)
Download or read book Mixed-Effects Models and Small Area Estimation written by Shonosuke Sugasawa and published by Springer Nature. This book was released on 2023-02-02 with total page 127 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a self-contained introduction of mixed-effects models and small area estimation techniques. In particular, it focuses on both introducing classical theory and reviewing the latest methods. First, basic issues of mixed-effects models, such as parameter estimation, random effects prediction, variable selection, and asymptotic theory, are introduced. Standard mixed-effects models used in small area estimation, known as the Fay-Herriot model and the nested error regression model, are then introduced. Both frequentist and Bayesian approaches are given to compute predictors of small area parameters of interest. For measuring uncertainty of the predictors, several methods to calculate mean squared errors and confidence intervals are discussed. Various advanced approaches using mixed-effects models are introduced, from frequentist to Bayesian approaches. This book is helpful for researchers and graduate students in fields requiring data analysis skills as well as in mathematical statistics.
Author : J. Kleffe
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (912 download)
Download or read book Working paper written by J. Kleffe and published by . This book was released on 1991 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : STANFORD UNIV CA DEPT OF STATISTICS.
Publisher :
ISBN 13 :
Total Pages : 11 pages
Book Rating : 4.:/5 (123 download)
Download or read book Mean Square Error Behavior for Prediction in Linear Regression Models written by STANFORD UNIV CA DEPT OF STATISTICS. and published by . This book was released on 1990 with total page 11 pages. Available in PDF, EPUB and Kindle. Book excerpt: For the problem of individual prediction in linear regression models, that is, estimation of a linear combination of regression coefficients, mean square error behavior of a general class of adaptive predictors is examined. (jhd).
Author : Mathias Lindholm
Publisher :
ISBN 13 :
Total Pages : 39 pages
Book Rating : 4.:/5 (13 download)
Download or read book Estimation of Conditional Mean Squared Error of Prediction for Claims Reserving written by Mathias Lindholm and published by . This book was released on 2019 with total page 39 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper studies estimation of conditional mean squared error of prediction, conditional on what is known at the time of prediction. The particular problem considered is the assessment of actuarial reserving methods given data in the form of runoff triangles (trapezoids), where the use of prediction assessment based on out-of-sample performance is not an option. The prediction assessment principle advocated here can be viewed as a generalization of Akaike's final prediction error. A direct application of this simple principle in the setting of a data generating process given in terms of a sequence of general linear models yields an estimator of conditional mean square error of prediction that can be computed explicitly for a wide range of models within this model class. Mack's distribution-free chain ladder model and the corresponding estimator of the prediction error for the ultimate claim amount is shown to be a special case. It is demonstrated that the prediction assessment principle easily applies to quite different data generating processes and results in estimators that have been studied in the literature.
Author : Dale L. Zimmerman
Publisher : Springer Nature
ISBN 13 : 3030520633
Total Pages : 504 pages
Book Rating : 4.0/5 (35 download)
Download or read book Linear Model Theory written by Dale L. Zimmerman and published by Springer Nature. This book was released on 2020-11-02 with total page 504 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook presents a unified and rigorous approach to best linear unbiased estimation and prediction of parameters and random quantities in linear models, as well as other theory upon which much of the statistical methodology associated with linear models is based. The single most unique feature of the book is that each major concept or result is illustrated with one or more concrete examples or special cases. Commonly used methodologies based on the theory are presented in methodological interludes scattered throughout the book, along with a wealth of exercises that will benefit students and instructors alike. Generalized inverses are used throughout, so that the model matrix and various other matrices are not required to have full rank. Considerably more emphasis is given to estimability, partitioned analyses of variance, constrained least squares, effects of model misspecification, and most especially prediction than in many other textbooks on linear models. This book is intended for master and PhD students with a basic grasp of statistical theory, matrix algebra and applied regression analysis, and for instructors of linear models courses. Solutions to the book’s exercises are available in the companion volume Linear Model Theory - Exercises and Solutions by the same author.
Author : Domingo Morales
Publisher : Springer Nature
ISBN 13 : 3030637573
Total Pages : 606 pages
Book Rating : 4.0/5 (36 download)
Download or read book A Course on Small Area Estimation and Mixed Models written by Domingo Morales and published by Springer Nature. This book was released on 2021-03-12 with total page 606 pages. Available in PDF, EPUB and Kindle. Book excerpt: This advanced textbook explores small area estimation techniques, covers the underlying mathematical and statistical theory and offers hands-on support with their implementation. It presents the theory in a rigorous way and compares and contrasts various statistical methodologies, helping readers understand how to develop new methodologies for small area estimation. It also includes numerous sample applications of small area estimation techniques. The underlying R code is provided in the text and applied to four datasets that mimic data from labor markets and living conditions surveys, where the socioeconomic indicators include the small area estimation of total unemployment, unemployment rates, average annual household incomes and poverty indicators. Given its scope, the book will be useful for master and PhD students, and for official and other applied statisticians.
Author : En-Tzu Tang
Publisher :
ISBN 13 :
Total Pages : 228 pages
Book Rating : 4.:/5 (8 download)
Download or read book On Estimation of the Mean Squared Error in Small Area Estimation and Related Topics written by En-Tzu Tang and published by . This book was released on 2008 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Charles E. McCulloch
Publisher : IMS
ISBN 13 : 9780940600546
Total Pages : 100 pages
Book Rating : 4.6/5 (5 download)
Download or read book Generalized Linear Mixed Models written by Charles E. McCulloch and published by IMS. This book was released on 2003 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: Wiley Series in Probability and Statistics A modern perspective on mixed models The availability of powerful computing methods in recent decades has thrust linear and nonlinear mixed models into the mainstream of statistical application. This volume offers a modern perspective on generalized, linear, and mixed models, presenting a unified and accessible treatment of the newest statistical methods for analyzing correlated, nonnormally distributed data. As a follow-up to Searle's classic, Linear Models, and Variance Components by Searle, Casella, and McCulloch, this new work progresses from the basic one-way classification to generalized linear mixed models. A variety of statistical methods are explained and illustrated, with an emphasis on maximum likelihood and restricted maximum likelihood. An invaluable resource for applied statisticians and industrial practitioners, as well as students interested in the latest results, Generalized, Linear, and Mixed Models features: * A review of the basics of linear models and linear mixed models * Descriptions of models for nonnormal data, including generalized linear and nonlinear models * Analysis and illustration of techniques for a variety of real data sets * Information on the accommodation of longitudinal data using these models * Coverage of the prediction of realized values of random effects * A discussion of the impact of computing issues on mixed models
Author : Jürgen Kleffe
Publisher :
ISBN 13 :
Total Pages : 17 pages
Book Rating : 4.:/5 (315 download)
Download or read book Estimation of Mean Square Error of Empirical Best Linear Unbiased Predictors Under a Random Error Variance Linear Model written by Jürgen Kleffe and published by . This book was released on 1991 with total page 17 pages. Available in PDF, EPUB and Kindle. Book excerpt:
