LEADER 05477nam 2200673Ia 450 001 9910146398803321 005 20230721021300.0 010 $a1-282-12322-X 010 $a9786612123221 010 $a0-470-74053-1 010 $a0-470-74054-X 035 $a(CKB)1000000000748754 035 $a(EBL)437418 035 $a(OCoLC)427565663 035 $a(SSID)ssj0000239023 035 $a(PQKBManifestationID)11175361 035 $a(PQKBTitleCode)TC0000239023 035 $a(PQKBWorkID)10238829 035 $a(PQKB)11709323 035 $a(MiAaPQ)EBC437418 035 $a(Au-PeEL)EBL437418 035 $a(CaPaEBR)ebr10307342 035 $a(CaONFJC)MIL212322 035 $a(EXLCZ)991000000000748754 100 $a20090227d2009 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aRobust methods in biostatistics$b[electronic resource] /$fStephane Heritier ... [et al.] 210 $aChichester, West Sussex ;$aHoboken $cJ. Wiley$d2009 215 $a1 online resource (294 p.) 225 1 $aWiley Series in Probability and Statistics ;$vv.825 300 $aDescription based upon print version of record. 311 $a0-470-02726-6 320 $aIncludes bibliographical references and index. 327 $aRobust Methods in Biostatistics; Contents; Preface; Acknowledgments; 1 Introduction; What is Robust Statistics?; Against What is Robust Statistics Robust?; Are Diagnostic Methods an Alternative to Robust Statistics? .; How do Robust Statistics Compare with Other Statistical Procedures in Practice?; 2 Key Measures and Results; Introduction; Statistical Tools for Measuring Robustness Properties; The Influence Function; The Breakdown Point; Geometrical Interpretation; The Rejection Point; General Approaches for Robust Estimation; The General Class of M-estimators; Properties of M-estimators 327 $aThe Class of S-estimatorsStatistical Tools for Measuring Tests Robustness; Sensitivity of the Two-sample t-test; Local Stability of a Test: the Univariate Case; Global Reliability of a Test: the Breakdown Functions; General Approaches for Robust Testing; Wald Test, Score Test and LRT; Geometrical Interpretation; General -type Classes of Tests; Asymptotic Distributions; Robustness Properties; 3 Linear Regression; Introduction; Estimating the Regression Parameters; The Regression Model; Robustness Properties of the LS and MLE Estimators; Glomerular Filtration Rate (GFR) Data Example 327 $aRobust EstimatorsGFR Data Example (continued); Testing the Regression Parameters; Significance Testing; Diabetes Data Example; Multiple Hypothesis Testing; Diabetes Data Example (continued); Checking and Selecting the Model; Residual Analysis; GFR Data Example (continued); Diabetes Data Example (continued); Coefficient of Determination; Global Criteria for Model Comparison; Diabetes Data Example (continued); Cardiovascular Risk Factors Data Example; 4 Mixed Linear Models; Introduction; The MLM; The MLM Formulation; Skin Resistance Data; Semantic Priming Data; Orthodontic Growth Data 327 $aClassical Estimation and InferenceMarginal and REML Estimation; Classical Inference; Lack of Robustness of Classical Procedures; Robust Estimation; Bounded Influence Estimators; S-estimators; MM-estimators; Choosing the Tuning Constants; Skin Resistance Data (continued); Robust Inference; Testing Contrasts; Multiple Hypothesis Testing of the Main Effects; Skin Resistance Data Example (continued); Semantic Priming Data Example (continued); Testing the Variance Components; Checking the Model; Detecting Outlying and Influential Observations; Prediction and Residual Analysis; Further Examples 327 $aMetallic Oxide DataOrthodontic Growth Data (continued); Discussion and Extensions; 5 Generalized Linear Models; Introduction; The GLM; Model Building; Classical Estimation and Inference for GLM; Hospital Costs Data Example; Residual Analysis; A Class of M-estimators for GLMs; Choice of ? and w(x); Fisher Consistency Correction; Nuisance Parameters Estimation; IF and Asymptotic Properties; Hospital Costs Example (continued); Robust Inference; Significance Testing and CIs; General Parametric Hypothesis Testing and Variable Selection; Hospital Costs Data Example (continued) 327 $aBreastfeeding Data Example 330 $aRobust statistics is an extension of classical statistics that specifically takes into account the concept that the underlying models used to describe data are only approximate. Its basic philosophy is to produce statistical procedures which are stable when the data do not exactly match the postulated models as it is the case for example with outliers. Robust Methods in Biostatistics proposes robust alternatives to common methods used in statistics in general and in biostatistics in particular and illustrates their use on many biomedical datasets. The methods introduced include robust 410 0$aWiley Series in Probability and Statistics 606 $aBiometry$xStatistical methods 606 $aBiomathematics 615 0$aBiometry$xStatistical methods. 615 0$aBiomathematics. 676 $a570.1/5195 676 $a570.15195 701 $aHeritier$b Stephane$0432026 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910146398803321 996 $aRobust methods in biostatistics$92172320 997 $aUNINA