LEADER 05339nam 2200637 a 450 001 9910876756203321 005 20200520144314.0 010 $a1-283-27998-3 010 $a9786613279989 010 $a1-118-16548-9 010 $a1-118-16549-7 035 $a(CKB)2550000000052763 035 $a(EBL)818924 035 $a(OCoLC)757511720 035 $a(SSID)ssj0000540950 035 $a(PQKBManifestationID)11327651 035 $a(PQKBTitleCode)TC0000540950 035 $a(PQKBWorkID)10492055 035 $a(PQKB)10150114 035 $a(MiAaPQ)EBC818924 035 $a(EXLCZ)992550000000052763 100 $a19890627d1990 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aRobust estimation and testing /$fRobert G. Staudte, Simon J. Sheather 210 $aNew York $cWiley$dc1990 215 $a1 online resource (382 p.) 225 1 $aWiley series in probability and mathematical statistics. Applied probability and statistics 300 $a"A Wiley-Interscience publication." 311 $a0-471-85547-2 320 $aIncludes bibliographical references and indexes. 327 $aRobust Estimation and Testing; Contents; 1. The Field of Statistics; 1.1 The Role of Statistics in Scientific Inference; 1.1.1 The Scientific Method; 1.1.2 Statistical Support for the Scientific Method; 1.1.3 The Significance of a Result; 1.1.4 The Challenge to Statisticians; 1.2 Recent Trends in Statistics; 1.2.1 Mathematical Statistics; 1.2.2 The Impact of Computers; 1.2.3 Robust Statistics; 1.3 The Case for Descriptive Measures; 1.3.1 Nonparametric Neighborhoods of Parametric Models; 1.3.2 Descriptive Measures; 1.4 The Domain and Range of This Book; 1.5 Problems; 1.6 Complements 327 $a1.6.1 Other Approaches to Robust Statistics1.6.2 Significance of an Experimental Result; 2. Estimating Scale-Finite Sample Results; 2.1 Examples; 2.2 Scale Parameter Families; 2.2.1 Definitions and Properties; 2.2.2 Examples of Continuous Scale Parameter Families; 2.3 Finite Sample Properties of Estimators; 2.3.1 Unbiasedness, Scale Equivariance, and Mean Squared Error; 2.3.2 Estimators of an Exponential Scale Parameter; 2.3.3 Mixture Models for Contamination; 2.3.4 Simulation Results; 2.3.5 Finite Sample Breakdown Point; 2.4 Standard Errors, the Bootstrap 327 $a2.4.1 Traditional Estimates of Standard Error2.4.2 Bootstrap Estimates of Standard Error; 2.4.3 An Illustration of Bootstrap Calculations; 2.4.4 Evaluating the Standard Error Estimates; 2.5 Problems; 2.6 Complements; 2.6.1 The Breakdown Point; 2.6.2 Further Developments on the Bootstrap; 3. Estimating Scale-Asymptotic Results; 3.1 Consistency, Asymptotic Normality, and Efficiency; 3.1.1 Representing Estimators by Descriptive Measures; 3.1.2 Consistency, Asymptotic Normality, and Relative Efficiency; 3.2 Robustness Concepts; 3.2.1 The Breakdown Point; 3.2.2 The Influence Function 327 $a3.2.3* L-Estimators3.2.4* Qualitative Robustness; 3.2.5 Concluding Remarks; 3.3 Descriptive Measures of Scale; 3.3.1 Measures of Scale; 3.3.2 Efficiency in Terms of Standardized Variance; 3.3.3 Simulation Results; 3.3.4 Summary; 3.4* Stability of Estimators on Neighborhoods of the Exponential Scale Parameter Family; 3.4.1 The Relative Efficiency Approach; 3.4.2 The Infinitesimal Approach; 3.5 Estimates of Standard Error; 3.5.1 Influence Function Estimates; 3.5.2 Bootstrap Estimates of Standard Error; 3.6 Problems; 3.7 Complements; 3.7.1 Sensitivity Curve 327 $a3.7.2 Resistant Estimates and Qualitative Robustness3.7.3 Standard and Nonstandard Errors; 4. Location-Dispersion Estimation; 4.1 Introduction and Examples; 4.1.1 Some Initial Questions; 4.1.2 Examples; 4.2 Location-Scale Parameter Families; 4.2.1 Definitions and Properties; 4.2.2 Examples of Location-Scale Families; 4.3 Estimators of Location; 4.3.1 Descriptive Measures of Location; 4.3.2 L-Estimators; 4.3.3 M-Estimators; 4.3.4 R-Estimators; 4.4 Estimators of Dispersion; 4.4.1 Descriptive Measures of Dispersion; 4.4.2 Performance of Some Dispersion Estimators 327 $a4.5 Joint Estimation of Location and Dispersion 330 $aAn introduction to the theory and methods of robust statistics, providing students with practical methods for carrying out robust procedures in a variety of statistical contexts and explaining the advantages of these procedures. In addition, the text develops techniques and concepts likely to be useful in the future analysis of new statistical models and procedures. Emphasizing the concepts of breakdown point and influence functon of an estimator, it demonstrates the technique of expressing an estimator as a descriptive measure from which its influence function can be derived and then used to 410 0$aWiley series in probability and mathematical statistics.$pApplied probability and statistics. 606 $aEstimation theory 606 $aRobust statistics 615 0$aEstimation theory. 615 0$aRobust statistics. 676 $a519.5/44 700 $aStaudte$b Robert G$0102311 701 $aSheather$b Simon J$0102312 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910876756203321 996 $aRobust estimation and testing$91127790 997 $aUNINA