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Statistical Inference - The Minimum Distance Approach (Paperback)
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Statistical Inference - The Minimum Distance Approach (Paperback)
Series: Chapman & Hall/CRC Monographs on Statistics and Applied Probability
Expected to ship within 12 - 17 working days
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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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