Robust Statistics: Theory and Methods (with R) (Wiley Series in Probability and Statistics) 2nd Edition Pdf written by Ricardo A. Maronna, R. Douglas Martin. A brand new edition of the popular text on strong data, entirely updated to add new and enhanced procedures and concentrate on execution of methodology employing the increasingly common open-source applications R. Classical statistics don’t deal nicely with outliers related to deviations from regular distributions. Strong statistical techniques take into consideration these deviations when estimating the parameters of parametric versions, thus raising the reliability of fitted versions and related inference. This new, second version of Robust Statistics: Theory and Techniques (with R) introduces a wide coverage of the concept of robust statistics that’s integrated with calculating procedures and software. Upgraded to incorporate important new study results of the past ten years and revolve around the usage of this favorite software bundle R, it sports comprehensive coverage of the vital methodology, such as regression, multivariate analysis, and time series modeling.
The book is illustrated throughout by a variety of examples and software which are supported by a companion site featuring data collections and R code which permit the reader to replicate the examples provided in the publication. It includes chapters on estimating scale and location; quantifying robustness; linear regression with fixed and with arbitrary predictors; multivariate evaluation; generalized linear models; time show; numerical calculations; and asymptotic concept of M-estimates. Explains both the usage and theoretical justification of powerful procedures. Guides readers in choosing and utilizing the most suitable robust procedures for their own problems. Features computational algorithms to the core procedures. Robust statistics study results of the previous decade contained in this 2nd edition include: rapid deterministic robust regression, finite-sample robustness, strong regularized regression, robust location and scatter estimation with missing information, robust estimation with separate outliers in factors, and strong combined linear models. Robust Statistics intends to stimulate the use of strong methods as a highly effective tool to raise the reliability and precision of statistical modelling and data evaluation.
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