{"product_id":"robust-statistics-theory-and-methods-855-wiley-series-in-probability-and-statistics-9780470010921","title":"Robust Statistics Theory and Methods 855 Wiley","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eRobust Statistics fills the need for a solid, up-to-date text that presents a broad overview of the theory of robust statistics, integrated with applications and computing. The book features in-depth coverage of the key methodology, including regression, multivariate analysis, and time series.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"This book belongs on the desk of every statistician working in robust statistics, and the authors are to be congratulated for providing the profession with a much-needed and valuable resource for teaching and research.\" (\u003ci\u003eJournal of the American Statistical Association\u003c\/i\u003e, June 2008)  \u003cp\u003e\"…an original and valuable contribution…a source of inspiration for all those pursuing research in robust statistics.\" (\u003ci\u003eMathematical Reviews\u003c\/i\u003e, 2007i)\u003c\/p\u003e \u003cp\u003e\"…a great book for graduate students as well as for applied scientists and data analysts.\" (\u003ci\u003eMAA Reviews\u003c\/i\u003e, February 14, 2007)\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003ePreface.\u003c\/b\u003e  \u003cp\u003e\u003cb\u003e1. Introduction.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Classical and robust approaches to statistics.\u003c\/p\u003e \u003cp\u003e1.2 Mean and standard deviation.\u003c\/p\u003e \u003cp\u003e1.3 The “three-sigma edit” rule.\u003c\/p\u003e \u003cp\u003e1.4 Linear regression.\u003c\/p\u003e \u003cp\u003e1.5 Correlation coefficients.\u003c\/p\u003e \u003cp\u003e1.6 Other parametric models.\u003c\/p\u003e \u003cp\u003e1.7 Problems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2. Location and Scale.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 The location model.\u003c\/p\u003e \u003cp\u003e2.2 M-estimates of location.\u003c\/p\u003e \u003cp\u003e2.3 Trimmed means.\u003c\/p\u003e \u003cp\u003e2.4 Dispersion estimates.\u003c\/p\u003e \u003cp\u003e2.5 M-estimates of scale.\u003c\/p\u003e \u003cp\u003e2.6 M-estimates of location with unknown dispersion.\u003c\/p\u003e \u003cp\u003e2.7 Numerical computation of M-estimates.\u003c\/p\u003e \u003cp\u003e2.8 Robust confidence intervals and tests.\u003c\/p\u003e \u003cp\u003e2.9 Appendix: proofs and complements.\u003c\/p\u003e \u003cp\u003e2.10 Problems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3. Measuring Robustness.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 The influence function.\u003c\/p\u003e \u003cp\u003e3.2 The breakdown point.\u003c\/p\u003e \u003cp\u003e3.3 Maximum asymptotic bias.\u003c\/p\u003e \u003cp\u003e3.4 Balancing robustness and efficiency.\u003c\/p\u003e \u003cp\u003e3.5 *“Optimal” robustness.\u003c\/p\u003e \u003cp\u003e3.6 Multidimensional parameters.\u003c\/p\u003e \u003cp\u003e3.7 *Estimates as functionals.\u003c\/p\u003e \u003cp\u003e3.8 Appendix: proofs of results.\u003c\/p\u003e \u003cp\u003e3.9 Problems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Linear Regression 1.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction.\u003c\/p\u003e \u003cp\u003e4.2 Review of the LS method.\u003c\/p\u003e \u003cp\u003e4.3 Classical methods for outlier detection.\u003c\/p\u003e \u003cp\u003e4.4 Regression M-estimates.\u003c\/p\u003e \u003cp\u003e4.5 Numerical computation of monotone M-estimates.\u003c\/p\u003e \u003cp\u003e4.6 Breakdown point of monotone regression estimates.\u003c\/p\u003e \u003cp\u003e4.7 Robust tests for linear hypothesis.\u003c\/p\u003e \u003cp\u003e4.8 *Regression quantiles.\u003c\/p\u003e \u003cp\u003e4.9 Appendix: proofs and complements.\u003c\/p\u003e \u003cp\u003e4.10 Problems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Linear Regression 2.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction.\u003c\/p\u003e \u003cp\u003e5.2 The linear model with random predictors 118\u003c\/p\u003e \u003cp\u003e5.3 M-estimates with a bounded \u003ci\u003eρ\u003c\/i\u003e-function.\u003c\/p\u003e \u003cp\u003e5.4 Properties of M-estimates with a bounded \u003ci\u003eρ\u003c\/i\u003e-function.\u003c\/p\u003e \u003cp\u003e5.5 MM-estimates.\u003c\/p\u003e \u003cp\u003e5.6 Estimates based on a robust residual scale.\u003c\/p\u003e \u003cp\u003e5.7 Numerical computation of estimates based on robust scales.\u003c\/p\u003e \u003cp\u003e5.8 Robust confidence intervals and tests for M-estimates.\u003c\/p\u003e \u003cp\u003e5.9 Balancing robustness and efficiency.\u003c\/p\u003e \u003cp\u003e5.10 The exact fit property.\u003c\/p\u003e \u003cp\u003e5.11 Generalized M-estimates.\u003c\/p\u003e \u003cp\u003e5.12 Selection of variables.\u003c\/p\u003e \u003cp\u003e5.13 Heteroskedastic errors.\u003c\/p\u003e \u003cp\u003e5.14 *Other estimates.\u003c\/p\u003e \u003cp\u003e5.15 Models with numeric and categorical predictors.\u003c\/p\u003e \u003cp\u003e5.16 *Appendix: proofs and complements.\u003c\/p\u003e \u003cp\u003e5.17 Problems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6. Multivariate Analysis.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction.\u003c\/p\u003e \u003cp\u003e6.2 Breakdown and efficiency of multivariate estimates.\u003c\/p\u003e \u003cp\u003e6.3 M-estimates.\u003c\/p\u003e \u003cp\u003e6.4 Estimates based on a robust scale.\u003c\/p\u003e \u003cp\u003e6.5 The Stahel–Donoho estimate.\u003c\/p\u003e \u003cp\u003e6.6 Asymptotic bias.\u003c\/p\u003e \u003cp\u003e6.7 Numerical computation of multivariate estimates.\u003c\/p\u003e \u003cp\u003e6.8 Comparing estimates.\u003c\/p\u003e \u003cp\u003e6.9 Faster robust dispersion matrix estimates.\u003c\/p\u003e \u003cp\u003e6.10 Robust principal components.\u003c\/p\u003e \u003cp\u003e6.11 *Other estimates of location and dispersion.\u003c\/p\u003e \u003cp\u003e6.12 Appendix: proofs and complements.\u003c\/p\u003e \u003cp\u003e6.13 Problems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7. Generalized Linear Models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Logistic regression.\u003c\/p\u003e \u003cp\u003e7.2 Robust estimates for the logistic model.\u003c\/p\u003e \u003cp\u003e7.3 Generalized linear models.\u003c\/p\u003e \u003cp\u003e7.4 Problems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8. Time Series.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Time series outliers and their impact.\u003c\/p\u003e \u003cp\u003e8.2 Classical estimates for AR models.\u003c\/p\u003e \u003cp\u003e8.3 Classical estimates for ARMA models.\u003c\/p\u003e \u003cp\u003e8.4 M-estimates of ARMA models.\u003c\/p\u003e \u003cp\u003e8.5 Generalized M-estimates.\u003c\/p\u003e \u003cp\u003e8.6 Robust AR estimation using robust filters.\u003c\/p\u003e \u003cp\u003e8.7 Robust model identification.\u003c\/p\u003e \u003cp\u003e8.8 Robust ARMA model estimation using robust filters.\u003c\/p\u003e \u003cp\u003e8.9 ARIMA and SARIMA models.\u003c\/p\u003e \u003cp\u003e8.10 Detecting time series outliers and level shifts.\u003c\/p\u003e \u003cp\u003e8.11 Robustness measures for time series.\u003c\/p\u003e \u003cp\u003e8.12 Other approaches for ARMA models.\u003c\/p\u003e \u003cp\u003e8.13 High-efficiency robust location estimates.\u003c\/p\u003e \u003cp\u003e8.14 Robust spectral density estimation.\u003c\/p\u003e \u003cp\u003e8.15 Appendix A: heuristic derivation of the asymptotic distribution of M-estimates for ARMA models.\u003c\/p\u003e \u003cp\u003e8.16 Appendix B: robust filter covariance recursions.\u003c\/p\u003e \u003cp\u003e8.17 Appendix C: ARMA model state-space representation.\u003c\/p\u003e \u003cp\u003e8.18 Problems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9. Numerical Algorithms.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Regression M-estimates.\u003c\/p\u003e \u003cp\u003e9.2 Regression S-estimates.\u003c\/p\u003e \u003cp\u003e9.3 The LTS-estimate.\u003c\/p\u003e \u003cp\u003e9.4 Scale M-estimates.\u003c\/p\u003e \u003cp\u003e9.5 Multivariate M-estimates.\u003c\/p\u003e \u003cp\u003e9.6 Multivariate S-estimates.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10. Asymptotic Theory of M-estimates.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Existence and uniqueness of solutions.\u003c\/p\u003e \u003cp\u003e10.2 Consistency.\u003c\/p\u003e \u003cp\u003e10.3 Asymptotic normality.\u003c\/p\u003e \u003cp\u003e10.4 Convergence of the SC to the IF.\u003c\/p\u003e \u003cp\u003e10.5 M-estimates of several parameters.\u003c\/p\u003e \u003cp\u003e10.6 Location M-estimates with preliminary scale.\u003c\/p\u003e \u003cp\u003e10.7 Trimmed means.\u003c\/p\u003e \u003cp\u003e10.8 Optimality of the MLE.\u003c\/p\u003e \u003cp\u003e10.9 Regression M-estimates.\u003c\/p\u003e \u003cp\u003e10.10 Nonexistence of moments of the sample median.\u003c\/p\u003e \u003cp\u003e10.11 Problems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11. Robust Methods in S-Plus.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Location M-estimates: function \u003ci\u003eMestimate.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.2 Robust regression.\u003c\/p\u003e \u003cp\u003e11.3 Robust dispersion matrices.\u003c\/p\u003e \u003cp\u003e11.4 Principal components.\u003c\/p\u003e \u003cp\u003e11.5 Generalized linear models.\u003c\/p\u003e \u003cp\u003e11.6 Time series.\u003c\/p\u003e \u003cp\u003e11.7 Public-domain software for robust methods.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12. Description of Data Sets.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eBibliography.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIndex.\u003c\/b\u003e\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402250559831,"sku":"9780470010921","price":78.26,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780470010921.jpg?v=1730479837","url":"https:\/\/bookcurl.com\/products\/robust-statistics-theory-and-methods-855-wiley-series-in-probability-and-statistics-9780470010921","provider":"Book Curl","version":"1.0","type":"link"}