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http://www.sudoc.fr/113527578 |
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20230103060900.0 |
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|a 0-470-01092-4
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|a 978-0-470-01092-1
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|a 9780470010921
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|a Robust statistics
|e theory and methods
|f Ricardo A. Maronna, R. Douglas Martin, Víctor J. Yohai
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|a Chichester, England
|c J. Wiley
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|d C 2006
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|a 1 volume (xx-403 pages)
|c illustrations
|d 24 cm
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|a Wiley series in probability and statistics
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|a Bibliographie pages [383]-396. Index.
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|b 1 Introduction.
|c 1.1 Classical and robust approaches to statistics.
|c 1.2 Mean and standard deviation.
|c 1.3 The "three-sigma edit" rule.
|c 1.4 Linear regression.
|c 1.5 Correlation coefficients.
|c 1.6 Other parametric models.
|c 1.7 Problems.
|b 2 Location and Scale.
|c 2.1 The location model.
|c 2.2 M-estimates of location.
|c 2.3 Trimmed means.
|c 2.4 Dispersion estimates.
|c 2.5 M-estimates of scale.
|c 2.6 M-estimates of location with unknown dispersion.
|c 2.7 Numerical computation of M-estimates.
|c 2.8 Robust confidence intervals and tests.
|c 2.9 Appendix: proofs and complements.
|c 2.10 Problems.
|b 3 Measuring Robustness.
|c 3.1 The influence function.
|c 3.2 The breakdown point.
|c 3.3 Maximum asymptotic bias.
|c 3.4 Balancing robustness and efficiency.
|c 3.5 "Optimal" robustness.
|c 3.6 Multidimensional parameters.
|c 3.7 Estimates as functionals.
|c 3.8 Appendix: proofs of results.
|c 3.9 Problems.
|b 4 Linear Regression
|c 4.1 Introduction.
|c 4.2 Review of the LS method.
|c 4.3 Classical methods for outlier detection.
|c 4.4 Regression M-estimates.
|c 4.5 Numerical computation of monotone M-estimates.
|c 4.6 Breakdown point of monotone regression estimates.
|c 4.7 Robust tests for linear hypothesis.
|c 4.8 Regression quantiles.
|c 4.9 Appendix: proofs and complements.
|c 4.10 Problems.
|b 5 Linear Regression
|c 5.1 Introduction.
|c 5.2 The linear model with random predictors 118
|c 5.3 M-estimates with a bounded rho-function.
|c 5.4 Properties of M-estimates with a bounded rho-function.
|c 5.5 MM-estimates.
|c 5.6 Estimates based on a robust residual scale.
|c 5.7 Numerical computation of estimates based on robust scales.
|c 5.8 Robust confidence intervals and tests for M-estimates.
|c 5.9 Balancing robustness and efficiency.
|c 5.10 The exact fit property.
|c 5.11 Generalized M-estimates.
|c 5.12 Selection of variables.
|c 5.13 Heteroskedastic errors.
|c 5.14 Other estimates.
|c 5.15 Models with numeric and categorical predictors.
|c 5.16 Appendix: proofs and complements.
|c 5.17 Problems.
|b 6 Multivariate Analysis.
|c 6.1 Introduction.
|c 6.2 Breakdown and efficiency of multivariate estimates.
|c 6.3 M-estimates.
|c 6.4 Estimates based on a robust scale.
|c 6.5 The Stahel-Donoho estimate.
|c 6.6 Asymptotic bias.
|c 6.7 Numerical computation of multivariate estimates.
|c 6.8 Comparing estimates.
|c 6.9 Faster robust dispersion matrix estimates.
|c 6.10 Robust principal components.
|c 6.11 Other estimates of location and dispersion.
|c 6.12 Appendix: proofs and complements.
|c 6.13 Problems.
|b 7 Generalized Linear Models.
|c 7.1 Logistic regression.
|c 7.2 Robust estimates for the logistic model.
|c 7.3 Generalized linear models.
|c 7.4 Problems.
|b 8 Time Series.
|c 8.1 Time series outliers and their impact.
|c 8.2 Classical estimates for AR models.
|c 8.3 Classical estimates for ARMA models.
|c 8.4 M-estimates of ARMA models.
|c 8.5 Generalized M-estimates.
|c 8.6 Robust AR estimation using robust filters.
|c 8.7 Robust model identification.
|c 8.8 Robust ARMA model estimation using robust filters.
|c 8.9 ARIMA and SARIMA models.
|c 8.10 Detecting time series outliers and level shifts.
|c 8.11 Robustness measures for time series.
|c 8.12 Other approaches for ARMA models.
|c 8.13 High-efficiency robust location estimates.
|c 8.14 Robust spectral density estimation.
|c 8.15 Appendix A: heuristic derivation of the asymptotic distribution of M-estimates for ARMA models.
|c 8.16 Appendix B: robust filter covariance recursions.
|c 8.17 Appendix C: ARMA model state-space representation.
|c 8.18 Problems.
|b 9 Numerical Algorithms.
|c 9.1 Regression M-estimates.
|c 9.2 Regression S-estimates.
|c 9.3 The LTS-estimate.
|c 9.4 Scale M-estimates.
|c 9.5 Multivariate M-estimates.
|c 9.6 Multivariate S-estimates.
|b 10 Asymptotic Theory of M-estimates.
|c 10.1 Existence and uniqueness of solutions.
|c 10.2 Consistency.
|c 10.3 Asymptotic normality.
|c 10.4 Convergence of the SC to the IF
|c 10.5 M-estimates of several parameters.
|c 10.6 Location M-estimates with preliminary scale.
|c 10.7 Trimmed means.
|c 10.8 Optimality of the MLE.
|c 10.9 Regression M-estimates.
|c 10.10 Nonexistence of moments of the sample median.
|c 10.11 Problems.
|b 11 Robust Methods in S-Plus.
|c 11.1 Location M-estimates: function Mestimate.
|c 11.2 Robust regression.
|c 11.3 Robust dispersion matrices.
|c 11.4 Principal components.
|c 11.5 Generalized linear models.
|c 11.6 Time series.
|c 11.7 Public-domain software for robust methods.
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| 410 |
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|0 060801735
|t Wiley series in probability and statistics
|x 1940-6517
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| 452 |
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| |
|0 170215784
|t Robust Statistics
|o Theory and Methods
|f Ricardo A. Maronna, R. Douglas Martin
|c West Sussex
|n John Wiley & Sons
|d 2006
|y 978-0-470-01092-1
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Nantilus
