Edition 
1st ed 
Descript 
1 online resource (424 pages) 

text txt rdacontent 

computer c rdamedia 

online resource cr rdacarrier 
Series 
Chapman and Hall/CRC Monographs on Statistics and Applied Probability Ser 

Chapman and Hall/CRC Monographs on Statistics and Applied Probability Ser

Note 
Front Cover  Dedication  Contents  Preface  Acknowledgments  1. Introduction  2. Statistical Distances  3. Continuous Models  4. Measures of Robustness and Computational Issues  5. The Hypothesis Testing Problem  6. Techniques for Inlier Modification  7. Weighted Likelihood Estimation  8. Multinomial GoodnessofFit Testing  9. The Density Power Divergence  10. Other Applications  11. Distance Measures in Information and Engineering  12. Applications to Other Models  Bibliography 

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 densitybased 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 semiparametric problems, mixture models, grouped data problems, and survival analysis. Statistical Inference: The Minimum Distance Approach gives a thorough account of densitybased minimum distance methods and their use in statistical inference. It covers statistical distances, densitybased 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 goodnessoffit tests. This carefully crafted resource is useful to researchers and scientists within and outside the statistics arena 

Description based on publisher supplied metadata and other sources 

Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2020. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries 
Link 
Print version: Basu, Ayanendranath Statistical Inference : The Minimum Distance Approach
London : CRC Press LLC,c2011 9781420099652

Subject 
Estimation theory.;Distances


Electronic books

Alt Author 
Shioya, Hiroyuki


Park, Chanseok

