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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.
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.
Asymptotic Statistics
Author: A. W. van der Vaart
Publisher: Cambridge University Press
ISBN: 0521784506
Pages: 443
Year: 2000-06-19
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A mathematically rigorous, practical introduction presenting standard topics plus research.
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.
Empirical Process Techniques for Dependent Data
Author: Herold Dehling, Thomas Mikosch, Michael Sörensen
Publisher: Springer Science & Business Media
ISBN: 1461200997
Pages: 383
Year: 2012-12-06
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Empirical process techniques for independent data have been used for many years in statistics and probability theory. These techniques have proved very useful for studying asymptotic properties of parametric as well as non-parametric statistical procedures. Recently, the need to model the dependence structure in data sets from many different subject areas such as finance, insurance, and telecommunications has led to new developments concerning the empirical distribution function and the empirical process for dependent, mostly stationary sequences. This work gives an introduction to this new theory of empirical process techniques, which has so far been scattered in the statistical and probabilistic literature, and surveys the most recent developments in various related fields. Key features: A thorough and comprehensive introduction to the existing theory of empirical process techniques for dependent data * Accessible surveys by leading experts of the most recent developments in various related fields * Examines empirical process techniques for dependent data, useful for studying parametric and non-parametric statistical procedures * Comprehensive bibliographies * An overview of applications in various fields related to empirical processes: e.g., spectral analysis of time-series, the bootstrap for stationary sequences, extreme value theory, and the empirical process for mixing dependent observations, including the case of strong dependence. To date this book is the only comprehensive treatment of the topic in book literature. It is an ideal introductory text that will serve as a reference or resource for classroom use in the areas of statistics, time-series analysis, extreme value theory, point process theory, and applied probability theory. Contributors: P. Ango Nze, M.A. Arcones, I. Berkes, R. Dahlhaus, J. Dedecker, H.G. Dehling,
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.
Agile Estimating and Planning
Author: Mike Cohn
Publisher: Pearson Education
ISBN: 0132703106
Pages: 368
Year: 2005-11-01
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Agile Estimating and Planning is the definitive, practical guide to estimating and planning agile projects. In this book, Agile Alliance cofounder Mike Cohn discusses the philosophy of agile estimating and planning and shows you exactly how to get the job done, with real-world examples and case studies. Concepts are clearly illustrated and readers are guided, step by step, toward how to answer the following questions: What will we build? How big will it be? When must it be done? How much can I really complete by then? You will first learn what makes a good plan-and then what makes it agile. Using the techniques in Agile Estimating and Planning , you can stay agile from start to finish, saving time, conserving resources, and accomplishing more. Highlights include: Why conventional prescriptive planning fails and why agile planning works How to estimate feature size using story points and ideal days–and when to use each How and when to re-estimate How to prioritize features using both financial and nonfinancial approaches How to split large features into smaller, more manageable ones How to plan iterations and predict your team's initial rate of progress How to schedule projects that have unusually high uncertainty or schedule-related risk How to estimate projects that will be worked on by multiple teams Agile Estimating and Planning supports any agile, semiagile, or iterative process, including Scrum, XP, Feature-Driven Development, Crystal, Adaptive Software Development, DSDM, Unified Process, and many more. It will be an indispensable resource for every development manager, team leader, and team member.
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.
Oracle Inequalities in Empirical Risk Minimization and Sparse Recovery Problems
Author: Vladimir Koltchinskii
Publisher: Springer
ISBN: 3642221475
Pages: 254
Year: 2011-07-29
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The purpose of these lecture notes is to provide an introduction to the general theory of empirical risk minimization with an emphasis on excess risk bounds and oracle inequalities in penalized problems. In recent years, there have been new developments in this area motivated by the study of new classes of methods in machine learning such as large margin classification methods (boosting, kernel machines). The main probabilistic tools involved in the analysis of these problems are concentration and deviation inequalities by Talagrand along with other methods of empirical processes theory (symmetrization inequalities, contraction inequality for Rademacher sums, entropy and generic chaining bounds). Sparse recovery based on l_1-type penalization and low rank matrix recovery based on the nuclear norm penalization are other active areas of research, where the main problems can be stated in the framework of penalized empirical risk minimization, and concentration inequalities and empirical processes tools have proved to be very useful.
Empirical Processes
Author: David Pollard
Publisher: IMS
ISBN: 0940600161
Pages: 86
Year: 1990-01-01
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Empirical Development Economics
Author: Måns Söderbom, Francis Teal, Markus Eberhardt, Simon Quinn, Andrew Zeitlin
Publisher: Routledge
ISBN: 1135093407
Pages: 434
Year: 2014-10-03
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Understanding why so many people across the world are so poor is one of the central intellectual challenges of our time. This book provides the tools and data that will enable students, researchers and professionals to address that issue. Empirical Development Economics has been designed as a hands-on teaching tool to investigate the causes of poverty. The book begins by introducing the quantitative approach to development economics. Each section uses data to illustrate key policy issues. Part One focuses on the basics of understanding the role of education, technology and institutions in determining why incomes differ so much across individuals and countries. In Part Two, the focus is on techniques to address a number of topics in development, including how firms invest, how households decide how much to spend on their children’s education, whether microcredit helps the poor, whether food aid works, who gets private schooling and whether property rights enhance investment. A distinctive feature of the book is its presentation of a range of approaches to studying development questions. Development economics has undergone a major change in focus over the last decade with the rise of experimental methods to address development issues; this book shows how these methods relate to more traditional ones. Please visit the book's website for more information: www.empiricalde.com
Quantile Processes with Statistical Applications
Author: Miklos Csorgo
Publisher: SIAM
ISBN: 1611970288
Pages: 156
Year: 1983-01-01
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Provides a comprehensive theory of the approximations of quantile processes in light of recent advances, as well as some of their statistical applications.
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.