Download or read book On Adaptive Scale equivariant M estimators in Linear Models written by Jana Jurečková and published by . This book was released on 1982 with total page 7 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 Adaptive Regression written by Yadolah Dodge and published by Springer Science & Business Media. This book was released on 2012-10-01 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: While there have been a large number of estimation methods proposed and developed for linear regression, none has proved good for all purposes. This text focuses on the construction of an adaptive combination of two estimation methods so as to help users make an objective choice and combine the desirable properties of two estimators.
Download or read book Robust Statistical Procedures written by Jana Jurecková and published by John Wiley & Sons. This book was released on 1996-04-19 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt: A broad and unified methodology for robust statistics—with exciting new applications Robust statistics is one of the fastest growing fields in contemporary statistics. It is also one of the more diverse and sometimes confounding areas, given the many different assessments and interpretations of robustness by theoretical and applied statisticians. This innovative book unifies the many varied, yet related, concepts of robust statistics under a sound theoretical modulation. It seamlessly integrates asymptotics and interrelations, and provides statisticians with an effective system for dealing with the interrelations between the various classes of procedures. Drawing on the expertise of researchers from around the world, and covering over a decade's worth of developments in the field, Robust Statistical Procedures: Asymptotics and Interrelations: Discusses both theory and applications in its two parts, from the fundamentals to robust statistical inference Thoroughly explores the interrelations between diverse classes of procedures, unlike any other book Compares nonparametric procedures with robust statistics, explaining in detail asymptotic representations for various estimators Provides a timesaving list of mathematical tools for the problems under discussion Keeps mathematical abstractions to a minimum, in spite of its largely theoretical content Includes useful problems and exercises at the end of each chapter Offers strategies for more complex models when using robust statistical procedures Self-contained and rounded in approach, this book is invaluable for both applied statisticians and theoretical researchers; for graduate students in mathematical statistics; and for anyone interested in the influence of this methodology.
Download or read book Theory and Applications of Sequential Nonparametrics written by Pranab Kumar Sen and published by SIAM. This book was released on 1985-01-01 with total page 106 pages. Available in PDF, EPUB and Kindle. Book excerpt: A study of sequential nonparametric methods emphasizing the unified Martingale approach to the theory, with a detailed explanation of major applications including problems arising in clinical trials, life-testing experimentation, survival analysis, classical sequential analysis and other areas of applied statistics and biostatistics.
Download or read book Linear Statistical Inference written by T. Calinski and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: An International Statistical Conference on Linear Inference was held in Poznan, Poland, on June 4-8, 1984. The conference was organized under the auspices of the Polish Section of the Bernoulli Society, the Committee of Mathematical Sciences and the Mathematical Institute of the ,Polish Academy of Sciences. The purpose of the meeting was to bring together scientists from vari ous countries working in the diverse areas of statistical sciences but showing great interest in the advances of research on linear inference taken in its broad sense. Thus, the conference programme included ses sions on Gauss-Markov models, robustness, variance components~ experi mental design, multiple comparisons, multivariate models, computational aspects and on some special topics. 38 papers were read within the vari ous sessions and 5 were presented as posters. At the end of the confer ence a lively general discussion session was held. The conference gathered more than ninety participants from 16 countries, representing both parts of Europe, North America and Asia. Judging from opinions expressed by many participants, the conference was quite suc cessful, well contributing to the dissemination of knowledge and the stimulation of research in different areas linked with statistical li near inference. If the conference was really a success, it was due to all its participants who in various ways were devoting their time and efforts to make the conference fruitful and enjoyable.
Download or read book Computational Aspects of Model Choice written by Jaromir Antoch and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although no-one is, probably, too enthused about the idea, it is a fact that the development of most empirical sciences to a great extend depends of the development of data analysis methods and techniques, which, due to the necessity of applications of computers for that pur pose, actually means that it practically depends on the advancements and orientation of computational statistics. This volume contains complete texts of the lectures held during the Summer School on "Computational Aspects of Model Choice" orga nized jointly by Charles University, Prague, and International Associa tion for Statistical Computing (IASC) on July 1-14, 1991, in Prague. Main aims of the Summer School were to review and analyse some of the recent developments concerning computational aspects of the model choice as well as their theoretical background. The topics covers the problems of the change point detection, robust estimation and its computational aspects, classification using binary trees, stochastic ap proximation and optimization including the discussion about available software, computational aspects of graphical model selection and mul tiple hypotheses testing. The bridge between these different approaches is formed by the survey paper about statistical applications of artificial intelligence.
Download or read book Methodology in Robust and Nonparametric Statistics written by Jana Jureckova and published by CRC Press. This book was released on 2012-07-20 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robust and nonparametric statistical methods have their foundation in fields ranging from agricultural science to astronomy, from biomedical sciences to the public health disciplines, and, more recently, in genomics, bioinformatics, and financial statistics. These disciplines are presently nourished by data mining and high-level computer-based algo
Download or read book Quantile Regression written by Roger Koenker and published by Cambridge University Press. This book was released on 2005-05-05 with total page 367 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantile regression is gradually emerging as a unified statistical methodology for estimating models of conditional quantile functions. By complementing the exclusive focus of classical least squares regression on the conditional mean, quantile regression offers a systematic strategy for examining how covariates influence the location, scale and shape of the entire response distribution. This monograph is the first comprehensive treatment of the subject, encompassing models that are linear and nonlinear, parametric and nonparametric. The author has devoted more than 25 years of research to this topic. The methods in the analysis are illustrated with a variety of applications from economics, biology, ecology and finance. The treatment will find its core audiences in econometrics, statistics, and applied mathematics in addition to the disciplines cited above.
Download or read book Rank Based Methods for Shrinkage and Selection written by A. K. Md. Ehsanes Saleh and published by John Wiley & Sons. This book was released on 2022-04-12 with total page 484 pages. Available in PDF, EPUB and Kindle. Book excerpt: Rank-Based Methods for Shrinkage and Selection A practical and hands-on guide to the theory and methodology of statistical estimation based on rank Robust statistics is an important field in contemporary mathematics and applied statistical methods. Rank-Based Methods for Shrinkage and Selection: With Application to Machine Learning describes techniques to produce higher quality data analysis in shrinkage and subset selection to obtain parsimonious models with outlier-free prediction. This book is intended for statisticians, economists, biostatisticians, data scientists and graduate students. Rank-Based Methods for Shrinkage and Selection elaborates on rank-based theory and application in machine learning to robustify the least squares methodology. It also includes: Development of rank theory and application of shrinkage and selection Methodology for robust data science using penalized rank estimators Theory and methods of penalized rank dispersion for ridge, LASSO and Enet Topics include Liu regression, high-dimension, and AR(p) Novel rank-based logistic regression and neural networks Problem sets include R code to demonstrate its use in machine learning
Download or read book Recent Results in Estimation Theory and Related Topics written by Edward J. Dudewicz and published by . This book was released on 1984 with total page 832 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Statistica Sinica written by and published by . This book was released on 1997 with total page 676 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Continuously Adaptive M estimation in the Linear Model written by Michael Conlon and published by . This book was released on 1982 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: M-estimates of regression parameters are found by minimizing the sum of a function of the difference between observed and predicted values of a dependent variable. The choice of a particular function before the data have been examined is shown to have serious consequences for the asymptotic variance of the parameter estimates. Previous adaptive M-estimates used one of a small number of functions selected after preliminary examination of the data. Continuously adaptive M-estimation (CAM) is introduced to choose a function according to maximum likelihood criteria from a continuous class of functions, thereby simultaneously estimating the regression parameters and the underlying error density. Algorithms for calculating the estimates are derived and numerical examples demonstrate the method's performance in a variety of regression problems, including symmetric and asymmetric errors.
Download or read book BEBR Faculty Working Paper written by and published by . This book was released on 1980 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Kybernetika written by and published by . This book was released on 1987 with total page 92 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Statistical Theory and Method Abstracts written by and published by . This book was released on 1999 with total page 786 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Probability and Mathematical Statistics written by and published by . This book was released on 1993 with total page 928 pages. Available in PDF, EPUB and Kindle. Book excerpt:
