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" Data analysis and approximate models : "
Laurie Davies
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BL
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| Record Number
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644478
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dltt
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| Main Entry
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Davies, Patrick Laurie
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| Title & Author
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Data analysis and approximate models : : model choice, location-scale, analysis of variance, nonparametic regression and image analysis /\ Laurie Davies
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| Series Statement
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Monographs on statistics and applied probability ;; 133
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| Page. NO
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xvi, 304 pages :: illustrations ;; 25 cm
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| ISBN
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9781482215861 (hardback)
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: 1482215861 (hardback)
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| Bibliographies/Indexes
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Includes bibliographical references and index
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| Abstract
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Data Analysis and Approximate Models: Model Choice, Location-Scale, Analysis of Variance, Nonparametric Regression and Image Analysis presents statistical analysis/inference based on approximate models. Developed by the author, this approach consistently treats models as approximations to data, not to some underlying truth. The author develops a concept of approximation for probability models with applications to: Discrete data, Location scale, Analysis of variance (ANOVA), Nonparametric regression, image analysis, and densities, Time series, Model choice, The book first highlights problems with concepts such as likelihood and efficiency and covers the definition of approximation and its consequences. A chapter on discrete data then presents the total variation metric as well as the Kullback-Leibler and chi-squared discrepancies as measures of fit. After focusing on outliers, the book discusses the location-scale problem, including approximation intervals, and gives a new treatment of higher-way ANOVA. The next several chapters describe novel procedures of nonparametric regression based on approximation. The final chapter offers a critique of statistics that covers likelihood, Bayesian statistics, sufficient statistics, efficiency, asymptotics, and model choice. Features, Provides one of the first accounts of statistical analysis and inference based on approximate models, Treats probability models as approximations in a consistent manner, Illustrates the importance of regularization, Evaluates the current definitions of one-, two-, three-, and higher-way ANOVA, Focuses on location, scale, and densities in nonparametric regression, Explains how to choose smoothing parameters in image analysis, Includes a new method for the analysis of contingency tables Book jacket
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| Subject
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Probabilities-- Philosophy
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| Subject
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Approximation theory
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| Dewey Classification
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519.5
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| LC Classification
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QA273.A35D38 2014
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