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Empirical Processes In M Estimation

Author: Sara A. Geer
Publisher: Cambridge University Press
ISBN: 9780521650021
Size: 38.10 MB
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Advanced text; estimation methods in statistics, e.g. least squares; lots of examples; minimal abstraction.

Asymptotic Statistics

Author: A. W. van der Vaart
Publisher: Cambridge University Press
ISBN: 9780521784504
Size: 65.32 MB
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A mathematically rigorous, practical introduction presenting standard topics plus research.

Empirical Processes With Applications To Statistics

Author: Galen R. Shorack
Publisher: SIAM
ISBN: 0898719011
Size: 17.52 MB
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Originally published in 1986, this valuable reference provides a detailed treatment of limit theorems and inequalities for empirical processes of real-valued random variables; applications of the theory to censored data, spacings, rank statistics, quantiles, and many functionals of empirical processes, including a treatment of bootstrap methods; and a summary of inequalities that are useful for proving limit theorems. At the end of the Errata section, the authors have supplied references to solutions for 11 of the 19 Open Questions provided in the book's original edition. Audience: researchers in statistical theory, probability theory, biostatistics, econometrics, and computer science.

Introduction To Empirical Processes And Semiparametric Inference

Author: Michael R. Kosorok
Publisher: Springer Science & Business Media
ISBN: 9780387749785
Size: 49.98 MB
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Kosorok’s brilliant text provides a self-contained introduction to empirical processes and semiparametric inference. These powerful research techniques are surprisingly useful for developing methods of statistical inference for complex models and in understanding the properties of such methods. This is an authoritative text that covers all the bases, and also a friendly and gradual introduction to the area. The book can be used as research reference and textbook.

Convergence Of Stochastic Processes

Author: David Pollard
Publisher: David Pollard
ISBN: 0387909907
Size: 72.37 MB
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Functionals on stochastic processes; Uniform convergence of empirical measures; Convergence in distribution in euclidean spaces; Convergence in distribution in metric spaces; The uniform metric on space of cadlag functions; The skorohod metric on D [0, oo); Central limit teorems; Martingales.

Empirical Likelihood

Author: Art B. Owen
Publisher: CRC Press
ISBN: 1420036157
Size: 23.48 MB
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Empirical likelihood provides inferences whose validity does not depend on specifying a parametric model for the data. Because it uses a likelihood, the method has certain inherent advantages over resampling methods: it uses the data to determine the shape of the confidence regions, and it makes it easy to combined data from multiple sources. It also facilitates incorporating side information, and it simplifies accounting for censored, truncated, or biased sampling. One of the first books published on the subject, Empirical Likelihood offers an in-depth treatment of this method for constructing confidence regions and testing hypotheses. The author applies empirical likelihood to a range of problems, from those as simple as setting a confidence region for a univariate mean under IID sampling, to problems defined through smooth functions of means, regression models, generalized linear models, estimating equations, or kernel smooths, and to sampling with non-identically distributed data. Abundant figures offer visual reinforcement of the concepts and techniques. Examples from a variety of disciplines and detailed descriptions of algorithms-also posted on a companion Web site at-illustrate the methods in practice. Exercises help readers to understand and apply the methods. The method of empirical likelihood is now attracting serious attention from researchers in econometrics and biostatistics, as well as from statisticians. This book is your opportunity to explore its foundations, its advantages, and its application to a myriad of practical problems.

Spline Functions Basic Theory

Author: Larry Schumaker
Publisher: Cambridge University Press
ISBN: 1139463438
Size: 27.88 MB
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This classic work continues to offer a comprehensive treatment of the theory of univariate and tensor-product splines. It will be of interest to researchers and students working in applied analysis, numerical analysis, computer science, and engineering. The material covered provides the reader with the necessary tools for understanding the many applications of splines in such diverse areas as approximation theory, computer-aided geometric design, curve and surface design and fitting, image processing, numerical solution of differential equations, and increasingly in business and the biosciences. This new edition includes a supplement outlining some of the major advances in the theory since 1981, and some 250 new references. It can be used as the main or supplementary text for courses in splines, approximation theory or numerical analysis.