Introduction to Robust Estimation and Hypothesis Testing

This book PDF is perfect for those who love Mathematics genre, written by Rand R. Wilcox and published by Academic Press which was released on 12 January 2012 with total hardcover pages 713. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Introduction to Robust Estimation and Hypothesis Testing books below.

Introduction to Robust Estimation and Hypothesis Testing
Author : Rand R. Wilcox
File Size : 49,6 Mb
Publisher : Academic Press
Language : English
Release Date : 12 January 2012
ISBN : 9780123869838
Pages : 713 pages
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Introduction to Robust Estimation and Hypothesis Testing by Rand R. Wilcox Book PDF Summary

"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"--

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

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Introduction to Robust Estimation and Hypothesis Testing

This revised book provides a thorough explanation of the foundation of robust methods, incorporating the latest updates on R and S-Plus, robust ANOVA (Analysis of Variance) and regression. It guides advanced students and other professionals through the basic strategies used for developing practical solutions to problems, and provides a brief

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Parameter Estimation and Hypothesis Testing in Linear Models

A treatment of estimating unknown parameters, testing hypotheses and estimating confidence intervals in linear models. Readers will find here presentations of the Gauss-Markoff model, the analysis of variance, the multivariate model, the model with unknown variance and covariance components and the regression model as well as the mixed model for

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Learning Statistics with R

"Learning Statistics with R" covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software and adopting a light, conversational style throughout. The book discusses how to get started in R, and gives an introduction to data

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This bookpresents material on both the analysis of the classical concepts of correlation and on the development of their robust versions, as well as discussing the related concepts of correlation matrices, partial correlation, canonical correlation, rank correlations, with the corresponding robust and non-robust estimation procedures. Every chapter contains a set

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Robust Estimation and Testing

An introduction to the theory and methods of robust statistics, providing students with practical methods for carrying out robust procedures in a variety of statistical contexts and explaining the advantages of these procedures. In addition, the text develops techniques and concepts likely to be useful in the future analysis of

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