Автор: Saleh Название: Theory of Ridge Regression Estimators with Applica tions ISBN: 1118644611 ISBN-13(EAN): 9781118644614 Издательство: Wiley Рейтинг: Цена: 16466.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
This book discusses current methods of estimation in linearmodels. In particular, the authors address the methodology oflinear multiple regression models that plays an important role inalmost every scientific investigations in various fields, including economics, engineering, and biostatistics. Thestandard estimation method for regression parameters is theordinary least square (OLS) principal where residual squared errorsare minimized. Applied statisticians are often not satisfied withOLS estimators when the design matrix is ill-conditioned, leadingto a multicollinearity problem and large variances that make the"prediction" inaccurate. This book details theridge regression estimator, which was developed to combat themulticollinearity problem. Another estimator, called theLiu-estimator due to Liu Kejian, is also addressed since itprovides a competing resolution to the multicollinearityproblem. The ridge regression estimators are complicatednon-linear functions of the ridge parameter, whereas, theLiu estimators are a linear function of the shrinkage parameter.With a focus on the ridge regression and LIU estimators, this bookprovides expanded coverage beyond the usual preliminary test andStein type estimator. In this case, we propose a class of compositeestimators that are obtained by multiplying the OLS, restrictedOLS, preliminary test OLS, and Stein-type OLS by the "ridgefactor" and "Liu-factor." This research is asignificant step towards the study of dominance properties as wellas the comparison of the extent of LASSO properties. In addition, research materials involving shrinkage and model selection forlinear regression models are provided. Topical coverageincludes: preliminaries; linear regression models; multipleregression models; measurement error models; generalized linearmodels; and autoregressive Gaussian processes.
Описание: "Peter [Bruce] has provided relevant, newsworthy and interesting examples, and, he has the reader doing all sorts of experiments ... for getting the feel of the statistical process. I believe the topics cover the waterfront of what a primer should consist of. " From a User at Statistics.
Автор: V.G. Voinov; M.S. Nikulin Название: Unbiased Estimators and their Applications ISBN: 0792339398 ISBN-13(EAN): 9780792339397 Издательство: Springer Рейтинг: Цена: 13275.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Contains problems of parametric point estimation for multivariate probability distributions emphasizing problems of unbiased estimation. This book covers some basic properties of multivariate continuous and discrete distributions, the general theory of point estimation in multivariate case, and techniques for constructing unbiased estimators.
Описание: Learn statistical methods quickly and easily with the discovery method With its emphasis on the discovery method, this publication encourages readers to discover solutions on their own rather than simply copy answers or apply a formula by rote.
Автор: S. N. Lahiri Название: Resampling Methods for Dependent Data ISBN: 1441918485 ISBN-13(EAN): 9781441918482 Издательство: Springer Рейтинг: Цена: 23058.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: By giving a detailed account of bootstrap methods and their properties for dependent data, this book provides illustrative numerical examples throughout.
Автор: Good Phillip I Название: Introduction to Statistics Through Resampling Methods and R ISBN: 1118428218 ISBN-13(EAN): 9781118428214 Издательство: Wiley Рейтинг: Цена: 8862.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: A highly accessible alternative approach to basic statistics Praise for the First Edition: "Certainly one of the most impressive little paperback 200-page introductory statistics books that I will ever see... it would make a good nightstand book for every statistician.
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