Statistical Inference : The Minimum Distance Approach

Ayanendranath Basu

ISBN 10: 1420099655 / ISBN 13: 9781420099652

Published by Chapman And Hall/CRC, 2011

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - This book gives a comprehensive account of density-based minimum distance methods and their use in statistical inference. It covers statistical distances, density-based minimum distance methods, discrete and continuous models, asymptotic distributions, robustness, computational issues, residual adjustment functions, graphical descriptions of robustness, penalized and combined distances, multisample methods, weighted likelihood, and multinomial goodness-of-fit tests. The book also introduces the minimum distance methodology in interdisciplinary areas, such as neural networks and image processing, as well as specialized models and problems, including regression, mixture models, survival and Bayesian analysis, and more.

Seller Inventory # 9781420099652

Bibliographic Details

• Title: Statistical Inference : The Minimum Distance Approach
• Publisher: Chapman And Hall/CRC
• Publication Date: 2011
• Language: English
• ISBN 10: 1420099655
• ISBN 13: 9781420099652
• Binding: Buch
• Condition: Neu
• Dimensions: 240W x 161H x 28D millimeters 240 millimeters width by 161 millimeters height by 28 millimeters depth
• Weight: 807 grams

About this title

Synopsis

In many ways, estimation by an appropriate minimum distance method is one of the most natural ideas in statistics. However, there are many different ways of constructing an appropriate distance between the data and the model: the scope of study referred to by "Minimum Distance Estimation" is literally huge. Filling a statistical resource gap, Statistical Inference: The Minimum Distance Approach comprehensively overviews developments in density-based minimum distance inference for independently and identically distributed data. Extensions to other more complex models are also discussed.

Comprehensively covering the basics and applications of minimum distance inference, this book introduces and discusses:

• The estimation and hypothesis testing problems for both discrete and continuous models
• The robustness properties and the structural geometry of the minimum distance methods
• The inlier problem and its possible solutions, and the weighted likelihood estimation problem
• The extension of the minimum distance methodology in interdisciplinary areas, such as neural networks and fuzzy sets, as well as specialized models and problems, including semi-parametric problems, mixture models, grouped data problems, and survival analysis.

Statistical Inference: The Minimum Distance Approach gives a thorough account of density-based minimum distance methods and their use in statistical inference. It covers statistical distances, density-based minimum distance methods, discrete and continuous models, asymptotic distributions, robustness, computational issues, residual adjustment functions, graphical descriptions of robustness, penalized and combined distances, weighted likelihood, and multinomial goodness-of-fit tests. This carefully crafted resource is useful to researchers and scientists within and outside the statistics arena.

About the Author

Ayanendranath Basu, Hiroyuki Shioya, Chanseok Park