Empirical Processes In M Estimation Cambridge Series In Statistical And Probabilistic Mathematics Book PDF, EPUB Download & Read Online Free

Empirical Processes in M-Estimation
Author: Sara A. Geer
Publisher: Cambridge University Press
ISBN: 052165002X
Pages: 286
Year: 2000-01-28
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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: 0521784506
Pages: 443
Year: 2000-06-19
View: 359
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A mathematically rigorous, practical introduction presenting standard topics plus research.
Empirical Processes with Applications to Statistics
Author: Galen R. Shorack, Jon A. Wellner
Publisher: SIAM
ISBN: 0898719011
Pages: 956
Year: 2009
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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: 0387749780
Pages: 483
Year: 2007-12-29
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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.
Lectures on Empirical Processes
Author: Eustasio Del Barrio, Paul Deheuvels, Sara A. Geer
Publisher: European Mathematical Society
ISBN: 3037190272
Pages: 254
Year: 2007-01-01
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Convergence of Stochastic Processes
Author: David Pollard
Publisher: David Pollard
ISBN: 0387909907
Pages: 215
Year: 1984
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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.
Mathematical Foundations of Infinite-Dimensional Statistical Models
Author:
Publisher:
ISBN: 1107043166
Pages:
Year:
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Empirical Likelihood
Author: Art B. Owen
Publisher: CRC Press
ISBN: 1420036157
Pages: 304
Year: 2001-05-18
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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
Pages:
Year: 2007-08-16
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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.
Nonparametric Estimation under Shape Constraints
Author: Piet Groeneboom, Geurt Jongbloed, Jon A. Wellner
Publisher: Cambridge University Press
ISBN: 0521864011
Pages: 428
Year: 2014-12-11
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This book introduces basic concepts of shape constrained inference and guides the reader to current developments in the subject.
Statistical Analysis of Stochastic Processes in Time
Author: J. K. Lindsey
Publisher: Cambridge University Press
ISBN: 113945451X
Pages:
Year: 2004-08-02
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This book was first published in 2004. Many observed phenomena, from the changing health of a patient to values on the stock market, are characterised by quantities that vary over time: stochastic processes are designed to study them. This book introduces practical methods of applying stochastic processes to an audience knowledgeable only in basic statistics. It covers almost all aspects of the subject and presents the theory in an easily accessible form that is highlighted by application to many examples. These examples arise from dozens of areas, from sociology through medicine to engineering. Complementing these are exercise sets making the book suited for introductory courses in stochastic processes. Software (available from www.cambridge.org) is provided for the freely available R system for the reader to apply to all the models presented.
Mathematical Statistics
Author: Aleksandr Petrovich Korostelev, Olga Korosteleva
Publisher: American Mathematical Soc.
ISBN: 0821852833
Pages: 246
Year: 2011
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This book is designed to bridge the gap between traditional textbooks in statistics and more advanced books that include the sophisticated nonparametric techniques. It covers topics in parametric and nonparametric large-sample estimation theory. The exposition is based on a collection of relatively simple statistical models. It gives a thorough mathematical analysis for each of them with all the rigorous proofs and explanations. The book also includes a number of helpful exercises. Prerequisites for the book include senior undergraduate/beginning graduate-level courses in probability and statistics.
Efficient and Adaptive Estimation for Semiparametric Models
Author: Peter J. Bickel, Chris A.J. Klaassen, Ya'acov Ritov, Jon A. Wellner
Publisher: Springer
ISBN: 0387984739
Pages: 588
Year: 1998-06-01
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This book deals with estimation in situations in which there is believed to be enough information to model parametrically some, but not all of the features of a data set. Such models have arisen in a wide context in recent years, and involve new nonlinear estimation procedures. Statistical models of this type are directly applicable to fields such as economics, epidemiology, and astronomy.
Asymptotics in Statistics
Author: Lucien Le Cam, Grace Lo Yang
Publisher: Springer Science & Business Media
ISBN: 1461211662
Pages: 287
Year: 2012-12-06
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This is the second edition of a coherent introduction to the subject of asymptotic statistics as it has developed over the past 50 years. It differs from the first edition in that it is now more 'reader friendly' and also includes a new chapter on Gaussian and Poisson experiments, reflecting their growing role in the field. Most of the subsequent chapters have been entirely rewritten and the nonparametrics of Chapter 7 have been amplified. The volume is not intended to replace monographs on specialized subjects, but will help to place them in a coherent perspective. It thus represents a link between traditional material - such as maximum likelihood, and Wald's Theory of Statistical Decision Functions -- together with comparison and distances for experiments. Much of the material has been taught in a second year graduate course at Berkeley for 30 years.
Large-Scale Inference
Author: Bradley Efron
Publisher: Cambridge University Press
ISBN: 1139492136
Pages:
Year: 2012-11-29
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We live in a new age for statistical inference, where modern scientific technology such as microarrays and fMRI machines routinely produce thousands and sometimes millions of parallel data sets, each with its own estimation or testing problem. Doing thousands of problems at once is more than repeated application of classical methods. Taking an empirical Bayes approach, Bradley Efron, inventor of the bootstrap, shows how information accrues across problems in a way that combines Bayesian and frequentist ideas. Estimation, testing and prediction blend in this framework, producing opportunities for new methodologies of increased power. New difficulties also arise, easily leading to flawed inferences. This book takes a careful look at both the promise and pitfalls of large-scale statistical inference, with particular attention to false discovery rates, the most successful of the new statistical techniques. Emphasis is on the inferential ideas underlying technical developments, illustrated using a large number of real examples.

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