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 : 40,8 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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Introduction to Robust Estimation and Hypothesis Testing

Introduction to Robust Estimating and Hypothesis Testing, 4th Editon, is a ‘how-to’ on the application of robust methods using available software. Modern robust methods provide improved techniques for dealing with outliers, skewed distribution curvature and heteroscedasticity that can provide substantial gains in power as well as a deeper, more accurate

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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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Robust Estimation and Hypothesis Testing

In statistical theory and practice, a certain distribution is usually assumed and then optimal solutions sought. Since deviations from an assumed distribution are very common, one cannot feel comfortable with assuming a particular distribution and believing it to be exactly correct. That brings the robustness issue in focus. In this

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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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Applying Contemporary Statistical Techniques

Applying Contemporary Statistical Techniques explains why traditional statistical methods are often inadequate or outdated when applied to modern problems. Wilcox demonstrates how new and more powerful techniques address these problems far more effectively, making these modern robust methods understandable, practical, and easily accessible. Highlights: * Assumes no previous training in statistics *

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Robustness Tests for Quantitative Research

This highly accessible book presents robustness testing as the methodology for conducting quantitative analyses in the presence of model uncertainty.

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