Download or read book An Asymptotic Expansion for M estimators in the Case of Scale Known written by J. N. W. Dachs and published by . This book was released on 1978 with total page 21 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Asymptotic Expansions for M estimators written by Jose Norberto Walter Dachs and published by . This book was released on 1973 with total page 72 pages. Available in PDF, EPUB and Kindle. Book excerpt: An asymptotic expansion for the distribution of M-estimators in the case of scale known, but with different basic assumptions, so as to include some of the M-estimators originally proposed. Also, a formal expansion for the case of scale unknown. Finally, several numerical results comparing, in part, values obtained with the Monte Carlo results of work done in Princeton.
Download or read book Asymptotic Distributions of Some Scale Estimators in Nonlinear Models With Long Memory Errors Having Infinite Variance written by and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Asymptotic Expansions for Studentized M estimators with Applications to Multiple Comparisons written by James Thomas Ringland and published by . This book was released on 1980 with total page 546 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Asymptotic Minimax Properties of M estimators for Scale written by Ka Ho Eden Wu and published by . This book was released on 1990 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book An Adaptive Choice of the Scale Parameter for M estimators written by Robert Michael Bell and published by . This book was released on 1980 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: Let x sub 1 to x sub n be a random sample from a distribution symmetric about the unknown location parameter theta. A major class of robust estimators of location is the class of M-estimators, each of which corresponds to a function psi defined on the reals. To be scale equivariant, these estimators require the use of a scale equivariant function of the sample. Commonly, this scale parameter is chosen to be a constant times the sample MAD (medial absolute deviation from the median). For a given function psi, the variance of the corresponding M-estimator vaires considerably with the value of the scale parameter. It is therefore proposed that the value which minimizes an estimate of the asymptotic variance of the M-estimator be used as the scaling factor. This adaptive method of scaling is shown to be asymptotically optimal (under fairly general conditions), in the sense that the resulting M-estimator has the smallest possible asymptotic variance among all M-estimators based on psi. In particular, when the underlying distribution is normal, the adaptive estimator based on any reasonable psi achieves full asymptotic efficiency, i.e., is asymptotically equivalent to the sample mean. The performance of the estimator for small samples is investigated by Monte Carlo methods for several choices of psi using the triefficiency criteria. A slight modification of the above estimator compares favorably with Tukey's bisquare M-estimator for sample sizes as small as 20. (Author).
Download or read book Asymptotic Distributions of M estimators in Non standard Cases written by Gabriel Pei and published by . This book was released on 1980 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Quarterly Publication of the American Statistical Association written by and published by . This book was released on 2005 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Government Reports Announcements written by and published by . This book was released on 1973 with total page 670 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Robust Statistical Procedures written by Peter J. Huber and published by SIAM. This book was released on 1996-01-01 with total page 77 pages. Available in PDF, EPUB and Kindle. Book excerpt: Here is a brief, well-organized, and easy-to-follow introduction and overview of robust statistics. Huber focuses primarily on the important and clearly understood case of distribution robustness, where the shape of the true underlying distribution deviates slightly from the assumed model (usually the Gaussian law). An additional chapter on recent developments in robustness has been added and the reference list has been expanded and updated from the 1977 edition.
Download or read book Robust Statistics written by Peter J. Huber and published by John Wiley & Sons. This book was released on 2011-09-20 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: A new edition of the classic, groundbreaking book on robust statistics Over twenty-five years after the publication of its predecessor, Robust Statistics, Second Edition continues to provide an authoritative and systematic treatment of the topic. This new edition has been thoroughly updated and expanded to reflect the latest advances in the field while also outlining the established theory and applications for building a solid foundation in robust statistics for both the theoretical and the applied statistician. A comprehensive introduction and discussion on the formal mathematical background behind qualitative and quantitative robustness is provided, and subsequent chapters delve into basic types of scale estimates, asymptotic minimax theory, regression, robust covariance, and robust design. In addition to an extended treatment of robust regression, the Second Edition features four new chapters covering: Robust Tests Small Sample Asymptotics Breakdown Point Bayesian Robustness An expanded treatment of robust regression and pseudo-values is also featured, and concepts, rather than mathematical completeness, are stressed in every discussion. Selected numerical algorithms for computing robust estimates and convergence proofs are provided throughout the book, along with quantitative robustness information for a variety of estimates. A General Remarks section appears at the beginning of each chapter and provides readers with ample motivation for working with the presented methods and techniques. Robust Statistics, Second Edition is an ideal book for graduate-level courses on the topic. It also serves as a valuable reference for researchers and practitioners who wish to study the statistical research associated with robust statistics.
Download or read book An Asymptotic Representation for M estimators and Linear Functions of Order Statistics written by Raymond J. Carroll and published by . This book was released on 1975 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Limiting Behavior of M estimators of Regression Coefficients in High Dimensional Linear Models written by Z.D. Bai and published by . This book was released on 1991 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Momentary Lapses written by Roger Koenker and published by . This book was released on 1991 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Robust Statistics written by Ricardo A. Maronna and published by John Wiley & Sons. This book was released on 2019-01-04 with total page 466 pages. Available in PDF, EPUB and Kindle. Book excerpt: A new edition of this popular text on robust statistics, thoroughly updated to include new and improved methods and focus on implementation of methodology using the increasingly popular open-source software R. Classical statistics fail to cope well with outliers associated with deviations from standard distributions. Robust statistical methods take into account these deviations when estimating the parameters of parametric models, thus increasing the reliability of fitted models and associated inference. This new, second edition of Robust Statistics: Theory and Methods (with R) presents a broad coverage of the theory of robust statistics that is integrated with computing methods and applications. Updated to include important new research results of the last decade and focus on the use of the popular software package R, it features in-depth coverage of the key methodology, including regression, multivariate analysis, and time series modeling. The book is illustrated throughout by a range of examples and applications that are supported by a companion website featuring data sets and R code that allow the reader to reproduce the examples given in the book. Unlike other books on the market, Robust Statistics: Theory and Methods (with R) offers the most comprehensive, definitive, and up-to-date treatment of the subject. It features chapters on estimating location and scale; measuring robustness; linear regression with fixed and with random predictors; multivariate analysis; generalized linear models; time series; numerical algorithms; and asymptotic theory of M-estimates. Explains both the use and theoretical justification of robust methods Guides readers in selecting and using the most appropriate robust methods for their problems Features computational algorithms for the core methods Robust statistics research results of the last decade included in this 2nd edition include: fast deterministic robust regression, finite-sample robustness, robust regularized regression, robust location and scatter estimation with missing data, robust estimation with independent outliers in variables, and robust mixed linear models. Robust Statistics aims to stimulate the use of robust methods as a powerful tool to increase the reliability and accuracy of statistical modelling and data analysis. It is an ideal resource for researchers, practitioners, and graduate students in statistics, engineering, computer science, and physical and social sciences.
Download or read book The Methodology and Practice of Econometrics written by Jennifer Castle and published by OUP Oxford. This book was released on 2009-04-30 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: David F. Hendry is a seminal figure in modern econometrics. He has pioneered the LSE approach to econometrics, and his influence is wide ranging. This book is a collection of papers dedicated to him and his work. Many internationally renowned econometricians who have collaborated with Hendry or have been influenced by his research have contributed to this volume, which provides a reflection on the recent advances in econometrics and considers the future progress for the methodology of econometrics. Central themes of the book include dynamic modelling and the properties of time series data, model selection and model evaluation, forecasting, policy analysis, exogeneity and causality, and encompassing. The book strikes a balance between econometric theory and empirical work, and demonstrates the influence that Hendry's research has had on the direction of modern econometrics. Contributors include: Karim Abadir, Anindya Banerjee, Gunnar Bårdsen, Andreas Beyer, Mike Clements, James Davidson, Juan Dolado, Jurgen Doornik, Robert Engle, Neil Ericsson, Jesus Gonzalo, Clive Granger, David Hendry, Kevin Hoover, Søren Johansen, Katarina Juselius, Steven Kamin, Pauline Kennedy, Maozu Lu, Massimiliano Marcellino, Laura Mayoral, Grayham Mizon, Bent Nielsen, Ragnor Nymoen, Jim Stock, Pravin Trivedi, Paolo Paruolo, Mark Watson, Hal White, and David Zimmer.
Download or read book Asymptotic Efficiency of Statistical Estimators Concepts and Higher Order Asymptotic Efficiency written by Masafumi Akahira and published by Springer. This book was released on 2011-11-22 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph is a collection of results recently obtained by the authors. Most of these have been published, while others are awaitlng publication. Our investigation has two main purposes. Firstly, we discuss higher order asymptotic efficiency of estimators in regular situa tions. In these situations it is known that the maximum likelihood estimator (MLE) is asymptotically efficient in some (not always specified) sense. However, there exists here a whole class of asymptotically efficient estimators which are thus asymptotically equivalent to the MLE. It is required to make finer distinctions among the estimators, by considering higher order terms in the expansions of their asymptotic distributions. Secondly, we discuss asymptotically efficient estimators in non regular situations. These are situations where the MLE or other estimators are not asymptotically normally distributed, or where l 2 their order of convergence (or consistency) is not n / , as in the regular cases. It is necessary to redefine the concept of asympto tic efficiency, together with the concept of the maximum order of consistency. Under the new definition as asymptotically efficient estimator may not always exist. We have not attempted to tell the whole story in a systematic way. The field of asymptotic theory in statistical estimation is relatively uncultivated. So, we have tried to focus attention on such aspects of our recent results which throw light on the area.
