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Chichester : John Wiley & Sons, c2006

xx,403 s. : il.

ISBN 0-470-01092-4 (váz.)

Wiley series in probability and statistics

Obsahuje ilustrace, grafy, rejstřík

Bibliografie na s. [383]-396

Metody statistické robustní - učebnice vysokošk.

Statistika robustní - učebnice vysokošk.

000090484

ROBUST STATISTICS // Theory and Methods // Ricardo A. Maronna - Universidad Nacional de La Plata, Argentina // R. Douglas Martin - University of Washington, Seattle, USA // Victor J. Yohai - University of Buenos Aires, Argentina // Classical statistical techniques fail to cope well with deviations from a standard distribution. Robust statistical methods take into account these deviations while estimating the parameters of parametric models, thus increasing the accuracy of the inference. Research into robust methods is flourishing, with new methods being developed and different applications considered. // Robust Statistics sets out to explain the use of robust methods and their theoretical justification. It provides an up-to-date overview of the theory and practical application of robust statistical methods in regression, multivariate analysis, generalized linear models and time series. This unique book: // Enables the reader to select and use the most appropriate robust method for their particular statistical model. // Features computational algorithms for the core methods. // Covers regression methods for data mining applications. // Includes examples with real data and applications using the S-Plus robust statistics library. // Describes the theoretical and operational aspects of robust methods separately, so the reader can choose to focus on one or the other. // Is supported by a supplementary website featuring datasets. // 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 ideal for researchers, practitioners and graduate students of statistics, electrical, chemical and biochemical engineering, and computer vision. There is also much to benefit researchers from other sciences, such as biotechnology, who need to use robust statistical methods in their work. // ©WILEY // wiley.com // ISBN 47fl-0-47G-GlG4a-l // Contents // Preface xv // 1 Introduction i // 1.1 Classical and robust approaches to statistics 1 // 1.2 Mean and standard deviation 2 // 1.3 The “three-sigma edit” rule 5 // 1.4 Linear regression 7 // 1.4.1 Straight-line regression 7 // 1.4.2 Multiple linear regression 9 // 1.5 Correlation coefficients 1 \\ // 1.6 Other parametric models 13 // 1.7 Problems I5 // 2 Location and Scale 17 // 2.1 The location model 17 // 2.2 M-estimates of location 22 // 2.2.1 Generalizing maximum likelihood 22 // 2.2.2 The distribution of M-estimates 25 // 2.2.3 An intuitive view of M-estimates 27 // 2.2.4 Redescending M-estimates 29 // 2.3 Trimmed means 31 // 2.4 Dispersion estimates 32 // 2.5 M-estimates of scale 34 // 2.6 M-estimates of location with unknown dispersion 36 // 2.6.1 Previous estimation of dispersion 37 // 2.6.2 Simultaneous M-estimates of location and dispersion 37 // 2.7 Numerical computation of M-estimates 39 // 2.7.1 Location with previously computed dispersion estimation 39 // 2.7.2 Scale estimates 40 // 2.7.3 Simultaneous

estimation of location and dispersion 41 // Vlil // CONTENTS // 2.8 Robust confidence intervals and tests 41 // 2.8.1 Confidence intervals 41 // 2.8.2 Tests 43 // 2.9 Appendix: proofs and complements 44 // 2.9.1 Mixtures 44 // 2.9.2 Asymptotic normality of M-estimates 45 // 2.9.3 Slutsky’s lemma 46 // 2.9.4 Quantiles 46 // 2.9.5 Alternative algorithms for M-estimates 46 // 2.10 Problems 48 // 3 Measuring Robustness 51 // 3.1 The influence function 55 // 3.1.1 *The convergence of the SC to the IF 57 // 3.2 The breakdown point 58 // 3.2.1 Location M-estimates 58 // 3.2.2 Scale and dispersion estimates 59 // 3.2.3 Location with previously computed dispersion estimate 60 // 3.2.4 Simultaneous estimation 60 // 3.2.5 Finite-sample breakdown point 61 // 3.3 Maximum asymptotic bias 62 // 3.4 Balancing robustness and efficiency 64 // 3.5 *“Optimal” robustness 65 // 3.5.1 Bias and variance optimality of location estimates 66 // 3.5.2 Bias optimality of scale and dispersion estimates 66 // 3.5.3 The infinitesimal approach 67 // 3.5.4 The Hampel approach 68 // 3.5.5 Balancing bias and variance: the general problem 70 // 3.6 Multidimensional parameters 70 // 3.7  Estimates as functionals 71 // 3.8 Appendix: proofs of results 75 // 3.8.1 IF of general M-estimates 75 // 3.8.2 Maximum BP of location estimates 76 // 3.8.3 BP of location M-estimates 76 // 3.8.4 Maximum bias of location M-estimates 78 // 3.8.5 The minimax bias property of the median 79 // 3.8.6 Minimizing the GES 80 // 3.8.7

Hampel optimality 82 // 3.9 Problems 84 // 4 Linear Regression 1 87 // 4.1 Introduction 87 // 4.2 Review of the LS method 91 // 4.3 Classical methods for outlier detection 94 // CONTENTS // ix // 4.4 Regression M-estimates 98 // 4.4.1 M-estimates with known scale 99 // 4.4.2 M-estimates with preliminary scale 100 // 4.4.3 Simultaneous estimation of regression and scale 103 // 4.5 Numerical computation of monotone M-estimates 103 // 4.5.1 The LI estimate 103 // 4.5.2 M-estimates with smooth  -function 104 // 4.6 Breakdown point of monotone regression estimates 105 // 4.7 Robust tests for linear hypothesis 107 // 4.7.1 Review of the classical theory 107 // 4.7.2 Robust tests using M-estimates 108 // 4.8 *Regression quantiles 110 // 4.9 Appendix: proofs and complements 110 // 4.9.1 Why equivariance? 110 // 4.9.2 Consistency of estimated slopes under asymmetric errors 111 // 4.9.3 Maximum EBP of equivariant estimates 112 // 4.9.4 The EBP of monotone M-estimates 113 // 4.10 Problems 114 // 5 Linear Regression 2 115 // 5.1 Introduction 115 // 5.2 The linear model with random predictors 118 // 5.3 M-estimates with a bounded p-function 119 // 5.4 Properties of M-estimates with a bounded p-function 120 // 5.4.1 Breakdown point 122 // 5.4.2 Influence function 123 // 5.4.3 Asymptotic normality 123 // 5.5 MM-estimates 124 // 5.6 Estimates based on a robust residual scale 126 // 5.6.1 S-estimates 129 // 5.6.2 L-estimates of scale and the LTS estimate 131 // 5.6.3 Improving efficiency with

one-step reweighting 132 // 5.6.4 A fully efficient one-step procedure 133 // 5.7 Numerical computation of estimates based on robust scales 134 // 5.7.1 Finding local minima 136 // 5.7.2 The subsampling algorithm 136 // 5.7.3 A strategy for fast iterative estimates 138 // 5.8 Robust confidence intervals and tests for M-estimates 139 // 5.8.1 Bootstrap robust confidence intervals and tests 141 // 5.9 Balancing robustness and efficiency 141 // 5.9.1 “Optimal” redescending M-estimates 144 // 5.10 The exact fit property 146 // 5.11 Generalized M-estimates 147 // 5.12 Selection of variables 150 // x // CONTENTS // 5.13 Heteroskedastic errors 153 // 5.13.1 Improving the efficiency of M-estimates 153 // 5.13.2 Estimating the asymptotic covariance matrix under // heteroskedastic errors 154 // 5.14 *Other estimates 156 // 5.14.1 r-estimates 156 // 5.14.2 Projection estimates 157 // 5.14.3 Constrained M-estimates 158 // 5.14.4 Maximum depth estimates 158 // 5.15 Models with numeric and categorical predictors 159 // 5.16 *Appendix: proofs and complements 162 // 5.16.1 The BP of monotone M-estimates with random X 162 // 5.16.2 Heavy-tailed x 162 // 5.16.3 Proof of the exact fit property 163 // 5.16.4 The BP of S-estimates 163 // 5.16.5 Asymptotic bias of M-estimates 166 // 5.16.6 Hampel optimality for GM-estimates 167 // 5.16.7 Justification of RFPE* 168 // 5.16.8 A robust multiple correlation coefficient 170 // 5.17 Problems 171 // 6 Multivariate Analysis 175 // 6.1 Introduction 175

// 6.2 Breakdown and efficiency of multivariate estimates 180 // 6.2.1 Breakdown point 180 // 6.2.2 The multivariate exact fit property 181 // 6.2.3 Efficiency 181 // 6.3 M-estimates 182 // 6.3.1 Collinearity 184 // 6.3.2 Size and shape 185 // 6.3.3 Breakdown point 186 // 6.4 Estimates based on a robust scale 187 // 6.4.1 The minimum volume ellipsoid estimate 187 // 6.4.2 S-estimates 188 // 6.4.3 The minimum covariance determinant estimate 189 // 6.4.4 S-estimates for high dimension 190 // 6.4.5 One-step reweighting 193 // 6.5 The Stahel-Donoho estimate 193 // 6.6 Asymptotic bias 195 // 6.7 Numerical computation of multivariate estimates 197 // 6.7.1 Monotone M-estimates 197 // 6.7.2 Local solutions for S-estimates 197 // 6.7.3 Subsampling for estimates based on a robust scale 198 // 6.7.4 The MVE 199 // 6.7.5 Computation of S-estimates 199 // CONTENTS xi // 6.7.6 The MCD 200 // 6.7.7 The Stahel-Donoho estimate 200 // 6.8 Comparing estimates 200 // 6.9 Faster robust dispersion matrix estimates 204 // 6.9.1 Using pairwise robust covariances 204 // 6.9.2 Using kurtosis 208 // 6.10 Robust principal components 209 // 6.10.1 Robust PC A based on a robust scale 211 // 6.10.2 Spherical principal components 212 // 6.11 *Other estimates of location and dispersion 214 // 6.11.1 Projection estimates 214 // 6.11.2 Constrained M-estimates 215 // 6.11.3 Multivariate MM- and r-estimates 216 // 6.11.4 Multivariate depth 216 // 6.12 Appendix: proofs and complements 216 // 6.12.1 Why affine equivariance?

216 // 6.12.2 Consistency of equivariant estimates 217 // 6.12.3 The estimating equations of the MLE 217 // 6.12.4 Asymptotic BP of monotone M-estimates 218 // 6.12.5 The estimating equations for S-estimates 220 // 6.12.6 Behavior of S-estimates for high p 221 // 6.12.7 Calculating the asymptotic covariance matrix of // location M-estimates 222 // 6.12.8 The exact fit property 224 // 6.12.9 Elliptical distributions 224 // 6.12.10 Consistency of Gnanadesikan-Kettenring correlations 225 // 6.12.11 Spherical principal components 226 // 6.13 Problems 227 // 7 Generalized Linear Models 229 // 7.1 Logistic regression 229 // 7.2 Robust estimates for the logistic model 233 // 7.2.1 Weighted MLEs 233 // 7.2.2 Redescending M-estimates 234 // 7.3 Generalized linear models 239 // 7.3.1 Conditionally unbiased bounded influence estimates 242 // 7.3.2 Other estimates for GLMs 243 // 7.4 Problems 244 // 8 Time Series 247 // 8.1 Time series outliers and their impact 247 // 8.1.1 Simple examples of outliers’ influence 250 // 8.1.2 Probability models for time series outliers 252 // 8.1.3 Bias impact of AOs 256 // CONTENTS // 8.2 Classical estimates for AR models 257 // 8.2.1 The Durbin-Levinson algorithm 260 // 8.2.2 Asymptotic distribution of classical estimates 262 // 8.3 Classical estimates for ARMA models 264 // 8.4 M-estimates of ARMA models 266 // 8.4.1 M-estimates and their asymptotic distribution 266 // 8.4.2 The behavior of M-estimates in AR processes with AOs 267 // 8.4.3 The behavior

of LS and M-estimates for ARMA // processes with infinite innovations variance 268 // 8.5 Generalized M-estimates 270 // 8.6 Robust AR estimation using robust filters 271 // 8.6.1 Naive minimum robust scale AR estimates 272 // 8.6.2 The robust filter algorithm 272 // 8.6.3 Minimum robust scale estimates based on robust filtering 275 // 8.6.4 A robust Durbin-Levinson algorithm 275 // 8.6.5 Choice of scale for the robust Durbin-Levinson procedure 276 // 8.6.6 Robust identification of AR order 277 // 8.7 Robust model identification 278 // 8.7.1 Robust autocorrelation estimates 278 // 8.7.2 Robust partial autocorrelation estimates 284 // 8.8 Robust ARMA model estimation using robust filters 287 // 8.8.1 ?-estimates of ARMA models 287 // 8.8.2 Robust filters for ARMA models 288 // 8.8.3 Robustly filtered r-estimates 290 // 8.9 ARIMA and SARIMA models 291 // 8.10 Detecting time series outliers and level shifts 294 // 8.10.1 Classical detection of time series outliers and level shifts 295 // 8.10.2 Robust detection of outliers and level shifts for // ARIMA models 297 // 8.10.3 REGARIMA models: estimation and outlier detection 300 // 8.11 Robustness measures for time series 301 // 8.11.1 Influence function 301 // 8.11.2 Maximum bias 303 // 8.11.3 Breakdown point 304 // 8.11.4 Maximum bias curves for the AR( 1 ) model 305 // 8.12 Other approaches for ARMA models 306 // 8.12.1 Estimates based on robust autocovariances 306 // 8.12.2 Estimates based on memory-m prediction residuals 308

8.13 High-efficiency robust location estimates 308 // 8.14 Robust spectral density estimation 309 // 8.14.1 Definition of the spectral density 309 // 8.14.2 AR spectral density 310 // 8.14.3 Classic spectral density estimation methods 311 // 8.14.4 Prewhitening 312 // 8.14.5 Influence of outliers on spectral density estimates 312 // 8.14.6 Robust spectral density estimation 314 // 8.14.7 Robust time-average spectral density estimate 316 // 8.15 Appendix A: heuristic derivation of the asymptotic distribution // of M-estimates for ARMA models 317 // 8.16 Appendix B: robust filter covariance recursions 320 // 8.17 Appendix C: ARMA model state-space representation 322 // 8.18 Problems 323 // 9 Numerical Algorithms 325 // 9.1 Regression M-estimates 325 // 9.2 Regression S-estimates 328 // 9.3 The LTS-estimate 328 // 9.4 Scale M-estimates 328 // 9.4.1 Convergence of the fixed point algorithm 328 // 9.4.2 Algorithms for the nonconcave case 330 // 9.5 Multivariate M-estimates 330 // 9.6 Multivariate S-estimates 331 // 9.6.1 S-estimates with monotone weights 331 // 9.6.2 The MCD 332 // 9.6.3 S-estimates with nonmonotone weights 333 // 9.6.4 *Proof of (9.25) 334 // 10 Asymptotic Theory of M-estimates 335 // 10.1 Existence and uniqueness of solutions 336 // 10.2 Consistency 337 // 10.3 Asymptotic normality 339 // 10.4 Convergence of the SC to the IF 342 // 10.5 M-estimates of several parameters 343 // 10.6 Location M-estimates with preliminary scale 346 // 10.7 Trimmed means 348 // 10.8

Optimality of the MLE 348 // 10.9 Regression M-estimates 350 // 10.9.1 Existence and uniqueness 350 // 10.9.2 Asymptotic normality: fixed X 351 // 10.9.3 Asymptotic normality: random X 355 // 10.10 Nonexistence of moments of the sample median 355 // 10.11 Problems 356 // 11 Robust Methods in S-Plus 357 // 11.1 Location M-estimates: function Mestimate 357 // 11.2 Robust regression 358 // 11.2.1 A general function for robust regression: ImRob 358 // 11.2.2 Categorical variables: functions as.factor and contrasts 361 // xiv CONTENTS // 11.2.3 Testing linear assumptions: function rob. linear test 363 // 11.2.4 Stepwise variable selection: function step 364 // 11.3 Robust dispersion matrices 365 // 11.3.1 A general function for computing robust // location-dispersion estimates: covRob 365 // 11.3.2 The SR-?’ estimate: function cov.SRocke 366 // 11.3.3 The bisquare S-estimate: function cov.Sbic 366 // 11.4 Principal components 366 // 11.4.1 Spherical principal components: function prin.comp.rob 367 // 11.4.2 Principal components based on a robust dispersion // matrix: function princomp.cov 367 // 11.5 Generalized linear models 368 // 11.5.1 M-estimate for logistic models: function BYlogreg 368 // 11.5.2 Weighted M-estimate: function WBYlogreg 369 // 11.5.3 A general function for generalized linear models: glmRob 370 // 11.6 Time series 371 // 11.6.1 GM-estimates for AR models: function argm 371 // 11.6.2 Fr-estimates and outlier detection for ARIMA and // REGARIMA models: function arima.rob

372 // 11.7 Public-domain software for robust methods 374 // 12 Description of Data Sets 377 // Bibliography 383 // Index 397