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Author: Moti Lal Tiku Publisher: Marcel Dekker ISBN: Category : Mathematics Languages : en Pages : 348
Book Description
This authoritative new volume treats a wide class of distributions that constitute plausible alternatives to normality -- such as short- and long-tailed symmetric distributions and moderately skewed distributions -- all having finite mean and variance. Robust Inference illustrates the appropriateness of various robust methods for solving both one-sample and multisample statistical inference problems ... develops Laguerre series expansions for Student's t and variance-ratio F statistic distributions ... analyzes normal and nonnormal distribution efficiencies ... works out modified maximum likelihood (MML) estimators based on type II censored samples for log-normal, logistic, exponential, and Rayleigh distributions ... uses MML estimators in constructing robust hypothesis-testing procedures ... considers the specialized topics of regression, analysis of variance, classification, and sample survey ... discusses goodness-of-fit tests ... describes Q-Q plots in a special appendix ... and much more. An outstanding, time-saving reference for theoreticians and practitioners of statistics, Robust Inference is also an excellent auxiliary text for an undergraduate- or graduate-level course on robustness. Book jacket.
Author: Moti Lal Tiku Publisher: Marcel Dekker ISBN: Category : Mathematics Languages : en Pages : 348
Book Description
This authoritative new volume treats a wide class of distributions that constitute plausible alternatives to normality -- such as short- and long-tailed symmetric distributions and moderately skewed distributions -- all having finite mean and variance. Robust Inference illustrates the appropriateness of various robust methods for solving both one-sample and multisample statistical inference problems ... develops Laguerre series expansions for Student's t and variance-ratio F statistic distributions ... analyzes normal and nonnormal distribution efficiencies ... works out modified maximum likelihood (MML) estimators based on type II censored samples for log-normal, logistic, exponential, and Rayleigh distributions ... uses MML estimators in constructing robust hypothesis-testing procedures ... considers the specialized topics of regression, analysis of variance, classification, and sample survey ... discusses goodness-of-fit tests ... describes Q-Q plots in a special appendix ... and much more. An outstanding, time-saving reference for theoreticians and practitioners of statistics, Robust Inference is also an excellent auxiliary text for an undergraduate- or graduate-level course on robustness. Book jacket.
Author: Ricardo A. Maronna Publisher: John Wiley & Sons ISBN: 1119214688 Category : Mathematics Languages : en Pages : 466
Book Description
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.
Author: G. E. P. Box Publisher: Academic Press ISBN: 1483259390 Category : Mathematics Languages : en Pages : 317
Book Description
Mathematics Research Center Symposium: Scientific Inference, Data Analysis, and Robustness focuses on the philosophy of statistical modeling, including model robust inference and analysis of data sets. The selection first elaborates on pivotal inference and the conditional view of robustness and some philosophies of inference and modeling, including ideas on modeling, significance testing, and scientific discovery. The book then ponders on parametric empirical Bayes confidence intervals, ecumenism in statistics, and frequency properties of Bayes rules. Discussions focus on consistency of Bayes rules, scientific method and the human brain, and statistical estimation and criticism. The book takes a look at the purposes and limitations of data analysis, likelihood, shape, and adaptive inference, statistical inference and measurement of entropy, and the robustness of a hierarchical model for multinomials and contingency tables. Topics include numerical results for contingency tables and robustness, multinomials, flattening constants, and mixed Dirichlet priors, entropy and likelihood, and test as measurement of entropy. The selection is a valuable reference for researchers interested in robust inference and analysis of data sets.
Author: Paul H. Garthwaite Publisher: OUP Oxford ISBN: 9780198572268 Category : Mathematics Languages : en Pages : 346
Book Description
Statistical inference is the foundation on which much of statistical practice is built. The book covers the topic at a level suitable for students and professionals who need to understand these foundations.
Author: Robert L. Launer Publisher: ISBN: Category : Mathematics Languages : en Pages : 330
Book Description
An introduction to robust estimation; The robustness of residual displays; Robust smoothing; Robust pitman-like estimators; Robust estimation in the presence of outliers; Study of robustness by simulation: particularly improvement by adjustment and combination; Robust techniques for the user; Application of robust regression to trajectory data reduction; Tests for censoring of extreme values (especially) when population distributions are incompletely defined; Robust estimation for time series autoregressions; Robust techniques in communication; Robustness in the strategy of scientific model building; A density-quantile function perspective on robust.
Author: Bruce Hansen Publisher: Princeton University Press ISBN: 0691235899 Category : Business & Economics Languages : en Pages : 1080
Book Description
The most authoritative and up-to-date core econometrics textbook available Econometrics is the quantitative language of economic theory, analysis, and empirical work, and it has become a cornerstone of graduate economics programs. Econometrics provides graduate and PhD students with an essential introduction to this foundational subject in economics and serves as an invaluable reference for researchers and practitioners. This comprehensive textbook teaches fundamental concepts, emphasizes modern, real-world applications, and gives students an intuitive understanding of econometrics. Covers the full breadth of econometric theory and methods with mathematical rigor while emphasizing intuitive explanations that are accessible to students of all backgroundsDraws on integrated, research-level datasets, provided on an accompanying websiteDiscusses linear econometrics, time series, panel data, nonparametric methods, nonlinear econometric models, and modern machine learningFeatures hundreds of exercises that enable students to learn by doingIncludes in-depth appendices on matrix algebra and useful inequalities and a wealth of real-world examplesCan serve as a core textbook for a first-year PhD course in econometrics and as a follow-up to Bruce E. Hansen’s Probability and Statistics for Economists
Author: Moti Lal Tiku
Author: Jay D. Wierer
Author: Moti Lal Tiku
Author: Ricardo A. Maronna
Author: William Wiant Davis
Author: Jonathan H. Wright
Author: G. E. P. Box
Author: Paul H. Garthwaite
Author: Robert L. Launer
Author: Bruce Hansen
Author: Trevor S. Breusch