Buch, Englisch, 216 Seiten, Format (B × H): 170 mm x 237 mm, Gewicht: 445 g
Buch, Englisch, 216 Seiten, Format (B × H): 170 mm x 237 mm, Gewicht: 445 g
ISBN: 978-1-58488-454-5
Verlag: Chapman and Hall/CRC
Robust statistical methods were developed to supplement the classical procedures when the data violate classical assumptions. They are ideally suited to applied research across a broad spectrum of study, yet most books on the subject are narrowly focused, overly theoretical, or simply outdated. Robust Statistical Methods with R provides a systematic treatment of robust procedures with an emphasis on practical application.The authors work from underlying mathematical tools to implementation, paying special attention to the computational aspects. They cover the whole range of robust methods, including differentiable statistical functions, distance of measures, influence functions, and asymptotic distributions, in a rigorous yet approachable manner. Highlighting hands-on problem solving, many examples and computational algorithms using the R software supplement the discussion. The book examines the characteristics of robustness, estimators of real parameter, large sample properties, and goodness-of-fit tests. It also includes a brief overview of R in an appendix for those with little experience using the software.Based on more than a decade of teaching and research experience, Robust Statistical Methods with R offers a thorough, detailed overview of robust procedures. It is an ideal introduction for those new to the field and a convenient reference for those who apply robust methods in their daily work.
Zielgruppe
Masters and Ph.D. students of statistics; statisticians, econometricians, mathematicians, and finance and insurance researchers.
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
INTRODUCTIONMATHEMATICAL TOOLS OF ROBUSTNESSStatistical ModelIllustration on Statistical EstimationStatistical FunctionalFisher's ConsistencySome Distances of Probability MeasuresRelations between DistancesDifferentiable Statistical FunctionalsGâteau DerivativeFréchet DerivativeHadamard (Compact) DerivativeLarge Sample Distribution of Empirical FunctionalComputation and Software NotesProblems and ComplementsBASIC CHARACTERISTICS OF ROBUSTNESSInfluence FunctionDiscretized Form of Influence FunctionQualitative RobustnessQuantitative Characteristics of Robustness Based on Influence FunctionMaximum BiasBreakdown PointTail-Behavior Measure of a Statistical EstimatorVariance of Asymptotic Normal DistributionProblems and ComplementsROBUST ESTIMATORS OF REAL PARAMETERIntroductionM-EstimatorsM-Estimator of Location ParameterFinite Sample Minimax Property of M-EstimatorMoment Convergence of M-EstimatorsStudentized M-EstimatorsL-EstimatorsSequential M- and L-EstimatorsR-EstimatorsNumerical IllustrationComputation and Software NotesProblems and ComplementsROBUST ESTIMATORS IN LINEAR MODELIntroductionLeast Squares MethodM-EstimatorsGM-EstimatorsS-Estimators and MM-EstimatorsL-Estimators, Regression QuantilesRegression Rank ScoresRobust Scale StatisticsEstimators with High Breakdown PointsOne-Step Versions of EstimatorsNumerical IllustrationsComputation and Software NotesProblems and ComplementsMULTIVARIATE LOCATION MODELIntroductionMultivariate M-Estimators of Location and ScatterHigh Breakdown Estimators of Multivariate Location and ScatterAdmissibility and ShrinkageNumerical Illustrations and Software NotesProblem and ComplementsSOME LARGE SAMPLE PROPERTIES OF ROBUST PROCEDURESIntroductionM-EstimatorsL-EstimatorsR-EstimatorsInterrelationships of M-, L-, and R-EstimatorsMinimaximally Robust EstimatorsProblems and ComplementsSOME GOODNESS-OF-FIT TESTSIntroductionTests of Normality of the Shapiro-Wilk Type with Nuisance Regression and Scale ParametersGoodness-of-Fit Tests for General Distribution with Nuisance Regression and ScaleNumerical IllustrationComputation and Software NotesAPPENDIX A: R SYSTEMBrief R OverviewREFERENCESSUBJECT INDEXAUTHOR INDEX
