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Empirical Processes with Applications to Statistics

Empirical Processes with Applications to Statistics
Author: Galen R. Shorack
Publisher: SIAM
Total Pages: 992
Release: 2009-01-01
Genre: Mathematics
ISBN: 0898719011

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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

Introduction to Empirical Processes and Semiparametric Inference
Author: Michael R. Kosorok
Publisher: Springer Science & Business Media
Total Pages: 482
Release: 2007-12-29
Genre: Mathematics
ISBN: 0387749780

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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.


Empirical Processes

Empirical Processes
Author: David Pollard
Publisher: IMS
Total Pages: 100
Release: 1990
Genre: Distribution (Probability theory).
ISBN: 9780940600164

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Empirical Process Techniques for Dependent Data

Empirical Process Techniques for Dependent Data
Author: Herold Dehling
Publisher: Springer Science & Business Media
Total Pages: 378
Release: 2012-12-06
Genre: Mathematics
ISBN: 1461200997

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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,


Empirical Processes in M-Estimation

Empirical Processes in M-Estimation
Author: Sara A. Geer
Publisher: Cambridge University Press
Total Pages: 302
Release: 2000-01-28
Genre: Business & Economics
ISBN: 9780521650021

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Advanced text; estimation methods in statistics, e.g. least squares; lots of examples; minimal abstraction.


Convergence of Stochastic Processes

Convergence of Stochastic Processes
Author: D. Pollard
Publisher: David Pollard
Total Pages: 223
Release: 1984-10-08
Genre: Mathematics
ISBN: 0387909907

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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.


Weak Convergence and Empirical Processes

Weak Convergence and Empirical Processes
Author: Aad van der vaart
Publisher: Springer Science & Business Media
Total Pages: 523
Release: 2013-03-09
Genre: Mathematics
ISBN: 1475725450

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This book explores weak convergence theory and empirical processes and their applications to many applications in statistics. Part one reviews stochastic convergence in its various forms. Part two offers the theory of empirical processes in a form accessible to statisticians and probabilists. Part three covers a range of topics demonstrating the applicability of the theory to key questions such as measures of goodness of fit and the bootstrap.


Principles of Nonparametric Learning

Principles of Nonparametric Learning
Author: Laszlo Györfi
Publisher: Springer
Total Pages: 344
Release: 2014-05-04
Genre: Technology & Engineering
ISBN: 3709125685

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This volume provides a systematic in-depth analysis of nonparametric learning. It covers the theoretical limits and the asymptotical optimal algorithms and estimates, such as pattern recognition, nonparametric regression estimation, universal prediction, vector quantization, distribution and density estimation, and genetic programming.


Statistical Models Based on Counting Processes

Statistical Models Based on Counting Processes
Author: Per K. Andersen
Publisher: Springer Science & Business Media
Total Pages: 779
Release: 2012-12-06
Genre: Mathematics
ISBN: 1461243483

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Modern survival analysis and more general event history analysis may be effectively handled within the mathematical framework of counting processes. This book presents this theory, which has been the subject of intense research activity over the past 15 years. The exposition of the theory is integrated with careful presentation of many practical examples, drawn almost exclusively from the authors'own experience, with detailed numerical and graphical illustrations. Although Statistical Models Based on Counting Processes may be viewed as a research monograph for mathematical statisticians and biostatisticians, almost all the methods are given in concrete detail for use in practice by other mathematically oriented researchers studying event histories (demographers, econometricians, epidemiologists, actuarial mathematicians, reliability engineers and biologists). Much of the material has so far only been available in the journal literature (if at all), and so a wide variety of researchers will find this an invaluable survey of the subject.


Probability, Random Variables, Statistics, and Random Processes

Probability, Random Variables, Statistics, and Random Processes
Author: Ali Grami
Publisher: John Wiley & Sons
Total Pages: 421
Release: 2019-04-02
Genre: Mathematics
ISBN: 1119300819

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Probability, Random Variables, Statistics, and Random Processes: Fundamentals & Applications is a comprehensive undergraduate-level textbook. With its excellent topical coverage, the focus of this book is on the basic principles and practical applications of the fundamental concepts that are extensively used in various Engineering disciplines as well as in a variety of programs in Life and Social Sciences. The text provides students with the requisite building blocks of knowledge they require to understand and progress in their areas of interest. With a simple, clear-cut style of writing, the intuitive explanations, insightful examples, and practical applications are the hallmarks of this book. The text consists of twelve chapters divided into four parts. Part-I, Probability (Chapters 1 – 3), lays a solid groundwork for probability theory, and introduces applications in counting, gambling, reliability, and security. Part-II, Random Variables (Chapters 4 – 7), discusses in detail multiple random variables, along with a multitude of frequently-encountered probability distributions. Part-III, Statistics (Chapters 8 – 10), highlights estimation and hypothesis testing. Part-IV, Random Processes (Chapters 11 – 12), delves into the characterization and processing of random processes. Other notable features include: Most of the text assumes no knowledge of subject matter past first year calculus and linear algebra With its independent chapter structure and rich choice of topics, a variety of syllabi for different courses at the junior, senior, and graduate levels can be supported A supplemental website includes solutions to about 250 practice problems, lecture slides, and figures and tables from the text Given its engaging tone, grounded approach, methodically-paced flow, thorough coverage, and flexible structure, Probability, Random Variables, Statistics, and Random Processes: Fundamentals & Applications clearly serves as a must textbook for courses not only in Electrical Engineering, but also in Computer Engineering, Software Engineering, and Computer Science.