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
View: 883
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Advanced text; estimation methods in statistics, e.g. least squares; lots of examples; minimal abstraction.
Introduction to Empirical Processes and Semiparametric Inference
Author: Michael R. Kosorok
Publisher: Springer Science & Business Media
ISBN: 0387749780
Pages: 483
Year: 2007-12-29
View: 267
Read: 1254
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
View: 589
Read: 1320

Asymptotic Statistics
Author: A. W. van der Vaart
Publisher: Cambridge University Press
ISBN: 0521784506
Pages: 443
Year: 2000-06-19
View: 1325
Read: 734
A mathematically rigorous, practical introduction presenting standard topics plus research.
Convergence of Stochastic Processes
Author: David Pollard
Publisher: David Pollard
ISBN: 0387909907
Pages: 215
Year: 1984
View: 913
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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
Pages: 304
Year: 2001-05-18
View: 459
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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.
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
View: 326
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This book introduces basic concepts of shape constrained inference and guides the reader to current developments in the subject.
System Parameter Identification
Author: Badong Chen, Yu Zhu, Jinchun Hu, Jose C. Principe
Publisher: Newnes
ISBN: 0124045952
Pages: 266
Year: 2013-07-17
View: 322
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Recently, criterion functions based on information theoretic measures (entropy, mutual information, information divergence) have attracted attention and become an emerging area of study in signal processing and system identification domain. This book presents a systematic framework for system identification and information processing, investigating system identification from an information theory point of view. The book is divided into six chapters, which cover the information needed to understand the theory and application of system parameter identification. The authors’ research provides a base for the book, but it incorporates the results from the latest international research publications. Named a 2013 Notable Computer Book for Information Systems by Computing Reviews One of the first books to present system parameter identification with information theoretic criteria so readers can track the latest developments Contains numerous illustrative examples to help the reader grasp basic methods
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
View: 605
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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.
Mathematical Foundations of Infinite-Dimensional Statistical Models
Author:
Publisher:
ISBN: 1107043166
Pages:
Year:
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Statistical Models
Author: A. C. Davison
Publisher: Cambridge University Press
ISBN: 1139437410
Pages:
Year: 2003-08-04
View: 186
Read: 417
Models and likelihood are the backbone of modern statistics. This 2003 book gives an integrated development of these topics that blends theory and practice, intended for advanced undergraduate and graduate students, researchers and practitioners. Its breadth is unrivaled, with sections on survival analysis, missing data, Markov chains, Markov random fields, point processes, graphical models, simulation and Markov chain Monte Carlo, estimating functions, asymptotic approximations, local likelihood and spline regressions as well as on more standard topics such as likelihood and linear and generalized linear models. Each chapter contains a wide range of problems and exercises. Practicals in the S language designed to build computing and data analysis skills, and a library of data sets to accompany the book, are available over the Web.
Large-Scale Inference
Author: Bradley Efron
Publisher: Cambridge University Press
ISBN: 1139492136
Pages:
Year: 2012-11-29
View: 573
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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.
Introduction to Empirical Processes and Semiparametric Inference
Author: Michael R. Kosorok
Publisher: Springer Science & Business Media
ISBN: 0387749780
Pages: 483
Year: 2007-12-29
View: 887
Read: 631
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.
Bayesian Nonparametrics
Author: Nils Lid Hjort, Chris Holmes, Peter Müller, Stephen G. Walker
Publisher: Cambridge University Press
ISBN: 1139484605
Pages:
Year: 2010-04-12
View: 822
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Bayesian nonparametrics works - theoretically, computationally. The theory provides highly flexible models whose complexity grows appropriately with the amount of data. Computational issues, though challenging, are no longer intractable. All that is needed is an entry point: this intelligent book is the perfect guide to what can seem a forbidding landscape. Tutorial chapters by Ghosal, Lijoi and Prünster, Teh and Jordan, and Dunson advance from theory, to basic models and hierarchical modeling, to applications and implementation, particularly in computer science and biostatistics. These are complemented by companion chapters by the editors and Griffin and Quintana, providing additional models, examining computational issues, identifying future growth areas, and giving links to related topics. This coherent text gives ready access both to underlying principles and to state-of-the-art practice. Specific examples are drawn from information retrieval, NLP, machine vision, computational biology, biostatistics, and bioinformatics.
Brownian Motion
Author: Peter Mörters, Yuval Peres
Publisher: Cambridge University Press
ISBN: 1139486578
Pages:
Year: 2010-03-25
View: 760
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This eagerly awaited textbook covers everything the graduate student in probability wants to know about Brownian motion, as well as the latest research in the area. Starting with the construction of Brownian motion, the book then proceeds to sample path properties like continuity and nowhere differentiability. Notions of fractal dimension are introduced early and are used throughout the book to describe fine properties of Brownian paths. The relation of Brownian motion and random walk is explored from several viewpoints, including a development of the theory of Brownian local times from random walk embeddings. Stochastic integration is introduced as a tool and an accessible treatment of the potential theory of Brownian motion clears the path for an extensive treatment of intersections of Brownian paths. An investigation of exceptional points on the Brownian path and an appendix on SLE processes, by Oded Schramm and Wendelin Werner, lead directly to recent research themes.

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