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

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9781420099652: Statistical Inference: The Minimum Distance Approach (Chapman & Hall/CRC Monographs on Statistics & 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.

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About the Author:

Indian Statistical Institute, Kolkata, India Muroran Institute of Technology, Muroran, Japan Clemson University, Clemson, SC, USA

Review:

"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

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Ayanendranath Basu, Hiroyuki Shioya, Chanseok Park
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Descripción Taylor Francis Ltd, United States, 2011. Hardback. Estado de conservación: New. Language: English . Brand New Book. 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. Nº de ref. de la librería AA69781420099652

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Descripción Taylor and Francis 2011-07-19, Boca Raton, FL, 2011. hardback. Estado de conservación: New. Nº de ref. de la librería 9781420099652

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Descripción 2011. HRD. Estado de conservación: New. New Book.Shipped from US within 10 to 14 business days. Established seller since 2000. Nº de ref. de la librería IB-9781420099652

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Descripción Chapman and Hall/CRC, 2017. Hardback. Estado de conservación: NEW. 9781420099652 This listing is a new book, a title currently in-print which we order directly and immediately from the publisher. Print on Demand title, produced to the highest standard, and there would be a delay in dispatch of around 10 working days. Nº de ref. de la librería HTANDREE0191704

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Descripción Taylor Francis Ltd, United States, 2011. Hardback. Estado de conservación: New. Language: English . This book usually ship within 10-15 business days and we will endeavor to dispatch orders quicker than this where possible. Brand New Book. 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. Nº de ref. de la librería BTE9781420099652

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Descripción Chapman & Hall, 2010. Hardcover. Estado de conservación: Brand New. 1st edition. 320 pages. 9.30x6.30x1.10 inches. In Stock. Nº de ref. de la librería __1420099655

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Descripción Hardcover. Estado de conservación: New. Hardcover. 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 dis.Shipping may be from multiple locations in the US or from the UK, depending on stock availability. 409 pages. 0.522. Nº de ref. de la librería 9781420099652

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