Statistical Inference

The Minimum Distance Approach

Ayanendranath Basu, Hiroyuki Shioya, Chanseok Park (Clemson University, Clemson, South Carolina, USA)

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English

Chapman & Hall/CRC
22 June 2011

Probability & statistics; Automatic control engineering; Environmental science, engineering & technology; Computing: general

Series: Chapman & Hall/CRC Monographs on Statistics and Applied Probability

"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."

By: Ayanendranath Basu, Hiroyuki Shioya, Chanseok Park (Clemson University, Clemson, South Carolina, USA)
Imprint: Chapman & Hall/CRC
Country of Publication: United Kingdom
Volume: 120
Dimensions: Height: 234mm, Width: 156mm, Spine: 18mm
Weight: 725g
ISBN: 9781420099652
ISBN 10: 1420099655
Series: Chapman & Hall/CRC Monographs on Statistics and Applied Probability
Pages: 430
Publication Date: 22 June 2011
Audience: College/higher education , General/trade , Primary , ELT Advanced
Format: Hardback
Publisher's Status: Active

Introduction. Statistical Distances. Continuous Models. Measures of Robustness and Computational Issues. The Hypothesis Testing Problem. Techniques for Inlier Modification. Weighted Likelihood Estimation. Multinomial Goodness-of-fit Testing. The Density Power Divergence. Other Applications. Distance Measures in Information and Engineering. Applications to Other Models.

Ayanendranath Basu, Hiroyuki Shioya, Chanseok Park

Reviews for Statistical Inference: The Minimum Distance Approach

The book is an excellent and thorough outline of work in the area. It would provide an ideal volume for someone who plans to undertake research in the area. -International Statistical Review, 2013 The book provides a comprehensive overview of the theory of density-based minimum distance methods and it is well written and easy to read and understand. The book is well suited for graduate students, professionals and researchers not only in statistics but also in biosciences, engineering and various other fields where statistical inference plays a fundamental role. -Alex Karagrigoriou, Journal of Applied Statistics, 2012