“Robust Statistics” - это новая версия популярного текста по робастной статистике, которая была тщательно обновлена для включения новых и улучшенных методов и акцентирования внимания на использовании методов методологии в популярном программном обеспечении R. Классическая статистика не всегда справляется с выбросами, связанными с отклонениями от стандартных распределений. Методы робастной статистики учитывают эти отклонения при оценке параметров параметрических моделей, повышая таким образом надежность построенных моделей и связанных выводов. В новом, втором издании “Robust Statistics: Теория и методы” с R представлен широкий охват теории робастной статистики, которая интегрирована с вычислительными методами и приложениями. Обновленное издание включает важные новые результаты исследований за последнее десятилетие и уделяет особое внимание использованию популярного программного пакета R, а также содержит углубленное рассмотрение ключевой методологии, включая регрессию, многомерный анализ и моделирование временных рядов. Книга проиллюстрирована множеством примеров и приложений, которые поддерживаются сайтом-компаньоном, на котором представлены наборы данных и код R, позволяющие читателю.
This is a new edition of a popular text dealing with robust statistics. It is thoroughly updated and now includes new and advanced methods for implementation. It also focuses on using software such as R, a popular open source program. Robust methods deal with outliers when analyzing data by considering them when estimating parameter values. This edition covers the broad scope of such methods, which includes regression, Monte Carlo analysis and time series prediction. Within each chapter, there are examples and analyses that can be reproduced using programs within the website. Along with standard uses for robust statistics there are chapters dedicated to finding the most robust variation of different methods such as linear regression and generalized regression models. The original concept of methods used within this book come from researches published 10 years ago. Free download code is provided online along with datasets to allow readers to replicate analyses in the text.
Электронная Книга «Robust Statistics» написана автором Ricardo Maronna A. в году.
Минимальный возраст читателя: 0
Язык: Английский
ISBN: 9781119214670
Описание книги от Ricardo Maronna A.
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.
