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008    200713s2011    xx      o     ||||0 eng d 
020    9781420099669|q(electronic bk.) 
020    |z9781420099652 
035    (MiAaPQ)EBC1633203 
035    (Au-PeEL)EBL1633203 
035    (CaPaEBR)ebr10502499 
035    (CaONFJC)MIL331148 
035    (OCoLC)740912851 
040    MiAaPQ|beng|erda|epn|cMiAaPQ|dMiAaPQ 
050  4 QA276.8 -- .B3925 2011eb 
082 0  519.54 
100 1  Basu, Ayanendranath 
245 10 Statistical Inference :|bThe Minimum Distance Approach 
250    1st ed 
264  1 London :|bCRC Press LLC,|c2011 
264  4 |c©2011 
300    1 online resource (424 pages) 
336    text|btxt|2rdacontent 
337    computer|bc|2rdamedia 
338    online resource|bcr|2rdacarrier 
490 1  Chapman and Hall/CRC Monographs on Statistics and Applied 
       Probability Ser 
505 0  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 
       Goodness-of-Fit Testing -- 9. The Density Power Divergence
       -- 10. Other Applications -- 11. Distance Measures in 
       Information and Engineering -- 12. Applications to Other 
       Models -- Bibliography 
520    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 
588    Description based on publisher supplied metadata and other
590    Electronic reproduction. Ann Arbor, Michigan : ProQuest 
       Ebook Central, 2020. Available via World Wide Web. Access 
       may be limited to ProQuest Ebook Central affiliated 
650  0 Estimation theory.;Distances 
655  4 Electronic books 
700 1  Shioya, Hiroyuki 
700 1  Park, Chanseok 
776 08 |iPrint version:|aBasu, Ayanendranath|tStatistical 
       Inference : The Minimum Distance Approach|dLondon : CRC 
       Press LLC,c2011|z9781420099652 
830  0 Chapman and Hall/CRC Monographs on Statistics and Applied 
       Probability Ser 
856 40 |uhttps://ebookcentral.proquest.com/lib/sinciatw/
       detail.action?docID=1633203|zClick to View