Download empirical processes in m estimation cambridge series in statistical and probabilistic mathematics in pdf or read empirical processes in m estimation cambridge series in statistical and probabilistic mathematics in pdf online books in PDF, EPUB and Mobi Format. Click Download or Read Online button to get empirical processes in m estimation cambridge series in statistical and probabilistic mathematics in pdf book now. This site is like a library, Use search box in the widget to get ebook that you want.



Empirical Processes In M Estimation

Author: Sara A. van de Geer
Publisher: Cambridge University Press
ISBN: 9780521650021
Size: 35.75 MB
Format: PDF, ePub
View: 5289
Download and Read
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: 15.70 MB
Format: PDF, ePub, Docs
View: 1075
Download and Read
A mathematically rigorous, practical introduction presenting standard topics plus research.

Mathematical Statistics

Author: Peter J. Bickel
Publisher: CRC Press
ISBN: 1498722709
Size: 79.73 MB
Format: PDF, Docs
View: 6201
Download and Read
Mathematical Statistics: Basic Ideas and Selected Topics, Volume II presents important statistical concepts, methods, and tools not covered in the authors’ previous volume. This second volume focuses on inference in non- and semiparametric models. It not only reexamines the procedures introduced in the first volume from a more sophisticated point of view but also addresses new problems originating from the analysis of estimation of functions and other complex decision procedures and large-scale data analysis. The book covers asymptotic efficiency in semiparametric models from the Le Cam and Fisherian points of view as well as some finite sample size optimality criteria based on Lehmann–Scheffé theory. It develops the theory of semiparametric maximum likelihood estimation with applications to areas such as survival analysis. It also discusses methods of inference based on sieve models and asymptotic testing theory. The remainder of the book is devoted to model and variable selection, Monte Carlo methods, nonparametric curve estimation, and prediction, classification, and machine learning topics. The necessary background material is included in an appendix. Using the tools and methods developed in this textbook, students will be ready for advanced research in modern statistics. Numerous examples illustrate statistical modeling and inference concepts while end-of-chapter problems reinforce elementary concepts and introduce important new topics. As in Volume I, measure theory is not required for understanding. Check out Volume I for fundamental, classical statistical concepts leading to the material in this volume.

Introduction To Empirical Processes And Semiparametric Inference

Author: Michael R. Kosorok
Publisher: Springer Science & Business Media
ISBN: 9780387749785
Size: 43.47 MB
Format: PDF, Kindle
View: 1396
Download and Read
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
Publisher: Cambridge University Press
ISBN: 1139484605
Size: 12.99 MB
Format: PDF
View: 7441
Download and Read
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.

Empirical Process Techniques For Dependent Data

Author: Herold Dehling
Publisher: Springer Science & Business Media
ISBN: 1461200997
Size: 16.28 MB
Format: PDF
View: 7467
Download and Read
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,

On The Estimation Of Multiple Random Integrals And U Statistics

Author: Péter Major
Publisher: Springer
ISBN: 3642376177
Size: 69.36 MB
Format: PDF, Kindle
View: 3561
Download and Read
This work starts with the study of those limit theorems in probability theory for which classical methods do not work. In many cases some form of linearization can help to solve the problem, because the linearized version is simpler. But in order to apply such a method we have to show that the linearization causes a negligible error. The estimation of this error leads to some important large deviation type problems, and the main subject of this work is their investigation. We provide sharp estimates of the tail distribution of multiple integrals with respect to a normalized empirical measure and so-called degenerate U-statistics and also of the supremum of appropriate classes of such quantities. The proofs apply a number of useful techniques of modern probability that enable us to investigate the non-linear functionals of independent random variables. This lecture note yields insights into these methods, and may also be useful for those who only want some new tools to help them prove limit theorems when standard methods are not a viable option.