# Statistical Inference: The Minimum Distance Approach (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) download epub

#### by **Hiroyuki Shioya,Ayanendranath Basu**

**Epub Book:**1228 kb. |

**Fb2 Book:**1618 kb.

by Ayanendranath Basu (Author), Hiroyuki Shioya (Author), Chanseok Park (Author) . Alex Karagrigoriou, Journal of Applied Statistics, 2012

by Ayanendranath Basu (Author), Hiroyuki Shioya (Author), Chanseok Park (Author) & 0 more. Alex Karagrigoriou, Journal of Applied Statistics, 2012. In many ways, estimation by an appropriate minimum distance method is one of the most natural ideas in statistics. 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.

Statistical Inference book. In many ways, estimation by an appropriate minimum distance method is one of the most natural ideas in statistics

Statistical Inference book. Statistical Inference: The Minimum Distance Approach (Chapman & Hall/Crc Monographs On Statistics & Applied Probability). 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.

Ayanendranath Basu, Hiroyuki Shioya, Chanseok Park.

Monographs on Statistics and Applied Probability 120. Statistical Inference. The Minimum Distance Approach. Indian Statistical Institute. Among several approaches to robust inference, we consider the minimum distance approach to estimate the parameters of the SN distribution by minimizing an appropriate divergence (distance) measure between the data and the model density.

Author : Ayanendranath Basu,Hiroyuki Shioya,Chanseok Park. Publisher : Chapman and Hall/CRC.

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By Ayanendranath Basu, Hiroyuki Shioya, Chanseok Park . Chapman and Hall/CRC. 429 pages 47 B/W Illus. For Instructors Request Inspection Copy. 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.

Monographs on Statistics and Applied Probability 12. Price: 8. 5. Statistical Distances Introduction Distances Based on Distribution Functions Density-Based Distances Minimum Hellinger Distance Estimation: Discrete Models Minimum Distance Estimation Based on Disparities: Discrete Models Some Examples. Continuous Models Introduction Minimum Hellinger Distance Estimation Estimation of Multivariate Location and Covariance A General Structure The Basu-Lindsay Approach for Continuous Data Examples.

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.

**Author:**Hiroyuki Shioya,Ayanendranath Basu

**ISBN:**1420099655

**Category:**Computers & Technology

**Subcategory:**Computer Science

**Language:**English

**Publisher:**Chapman and Hall/CRC; 1 edition (June 22, 2011)

**Pages:**429 pages