Robust Estimation and Hypothesis Testing

This book PDF is perfect for those who love Estimation theory genre, written by Moti Lal Tiku and published by New Age International which was released on 27 April 2024 with total hardcover pages 22. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Robust Estimation and Hypothesis Testing books below.

Robust Estimation and Hypothesis Testing
Author : Moti Lal Tiku
File Size : 41,6 Mb
Publisher : New Age International
Language : English
Release Date : 27 April 2024
ISBN : 9788122415568
Pages : 22 pages
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Robust Estimation and Hypothesis Testing by Moti Lal Tiku Book PDF Summary

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 book, we have given statistical procedures which are robust to plausible deviations from an assumed mode. The method of modified maximum likelihood estimation is used in formulating these procedures. The modified maximum likelihood estimators are explicit functions of sample observations and are easy to compute. They are asymptotically fully efficient and are as efficient as the maximum likelihood estimators for small sample sizes. The maximum likelihood estimators have computational problems and are, therefore, elusive. A broad range of topics are covered in this book. Solutions are given which are easy to implement and are efficient. The solutions are also robust to data anomalies: outliers, inliers, mixtures and data contaminations. Numerous real life applications of the methodology are given.

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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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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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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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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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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Testing For Normality

Describes the selection, design, theory, and application of tests for normality. Covers robust estimation, test power, and univariate and multivariate normality. Contains tests ofr multivariate normality and coordinate-dependent and invariant approaches.

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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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