文件名称:
Robust statistics: Theory and Methods
开发工具:
文件大小: 4mb
下载次数: 0
上传时间: 2009-11-06
详细说明： Robust Statistics: Theory and Methods (Wiley Series in Probability and Statistics) (Hardcover) Preface. 1. Introduction. 1.1 Classical and robust approaches to statistics. 1.2 Mean and standard deviation. 1.3 The “three-sigma edit” rule. 1.4 Linear reg ression. 1.5 Correlation coefficients. 1.6 Other parametric models. 1.7 Problems. 2. Location and Scale. 2.1 The location model. 2.2 M-estimates of location. 2.3 Trimmed means. 2.4 Dispersion estimates. 2.5 M-estimates of scale. 2.6 M-estimates of location with unknown dispersion. 2.7 Numerical computation of M-estimates. 2.8 Robust confidence intervals and tests. 2.9 Appendix: proofs and complements. 2.10 Problems. 3. Measuring Robustness. 3.1 The influence function. 3.2 The breakdown point. 3.3 Maximum asymptotic bias. 3.4 Balancing robustness and efficiency. 3.5 *“Optimal” robustness. 3.6 Multidimensional parameters. 3.7 *Estimates as functionals. 3.8 Appendix: proofs of results. 3.9 Problems. 4 Linear Regression 1. 4.1 Introduction. 4.2 Review of the LS method. 4.3 Classical methods for outlier detection. 4.4 Regression M-estimates. 4.5 Numerical computation of monotone M-estimates. 4.6 Breakdown point of monotone regression estimates. 4.7 Robust tests for linear hypothesis. 4.8 *Regression quantiles. 4.9 Appendix: proofs and complements. 4.10 Problems. 5 Linear Regression 2. 5.1 Introduction. 5.2 The linear model with random predictors 118 5.3 M-estimates with a bounded ρ-function. 5.4 Properties of M-estimates with a bounded ρ-function. 5.5 MM-estimates. 5.6 Estimates based on a robust residual scale. 5.7 Numerical computation of estimates based on robust scales. 5.8 Robust confidence intervals and tests for M-estimates. 5.9 Balancing robustness and efficiency. 5.10 The exact fit property. 5.11 Generalized M-estimates. 5.12 Selection of variables. 5.13 Heteroskedastic errors. 5.14 *Other estimates. 5.15 Models with numeric and categorical predictors. 5.16 *Appendix: proofs and complements. 5.17 Problems. 6. Multivariate Analysis. 6.1 Introduction. 6.2 Breakdown and efficiency of multivariate estimates. 6.3 M-estimates. 6.4 Estimates based on a robust scale. 6.5 The Stahel–Donoho estimate. 6.6 Asymptotic bias. 6.7 Numerical computation of multivariate estimates. 6.8 Comparing estimates. 6.9 Faster robust dispersion matrix estimates. 6.10 Robust principal components. 6.11 *Other estimates of location and dispersion. 6.12 Appendix: proofs and complements. 6.13 Problems. 7. Generalized Linear Models. 7.1 Logistic regression. 7.2 Robust estimates for the logistic model. 7.3 Generalized linear models. 7.4 Problems. 8. Time Series. 8.1 Time series outliers and their impact. 8.2 Classical estimates for AR models. 8.3 Classical estimates for ARMA models. 8.4 M-estimates of ARMA models. 8.5 Generalized M-estimates. 8.6 Robust AR estimation using robust filters. 8.7 Robust model identification. 8.8 Robust ARMA model estimation using robust filters. 8.9 ARIMA and SARIMA models. 8.10 Detecting time series outliers and level shifts. 8.11 Robustness measures for time series. 8.12 Other approaches for ARMA models. 8.13 High-efficiency robust location estimates. 8.14 Robust spectral density estimation. 8.15 Appendix A: heuristic derivation of the asymptotic distribution of M-estimates for ARMA models. 8.16 Appendix B: robust filter covariance recursions. 8.17 Appendix C: ARMA model state-space representation. 8.18 Problems. 9. Numerical Algorithms. 9.1 Regression M-estimates. 9.2 Regression S-estimates. 9.3 The LTS-estimate. 9.4 Scale M-estimates. 9.5 Multivariate M-estimates. 9.6 Multivariate S-estimates. 10. Asymptotic Theory of M-estimates. 10.1 Existence and uniqueness of solutions. 10.2 Consistency. 10.3 Asymptotic normality. 10.4 Convergence of the SC to the IF. 10.5 M-estimates of several parameters. 10.6 Location M-estimates with preliminary scale. 10.7 Trimmed means. 10.8 Optimality of the MLE. 10.9 Regression M-estimates. 10.10 Nonexistence of moments of the sample median. 10.11 Problems. 11. Robust Methods in S-Plus. 11.1 Location M-estimates: function Mestimate. 11.2 Robust regression. 11.3 Robust dispersion matrices. 11.4 Principal components. 11.5 Generalized linear models. 11.6 Time series. 11.7 Public-domain software for robust methods. 12. Description of Data Sets. Bibliography. Index. ...展开收缩
(系统自动生成,下载前可以参看下载内容)
下载文件列表
相关说明
- 本站资源为会员上传分享交流与学习,如有侵犯您的权益,请联系我们删除.
- 本站是交换下载平台，提供交流渠道，下载内容来自于网络，除下载问题外，其它问题请自行百度。
- 本站已设置防盗链，请勿用迅雷、QQ旋风等多线程下载软件下载资源，下载后用WinRAR最新版进行解压.
- 如果您发现内容无法下载，请稍后再次尝试；或者到消费记录里找到下载记录反馈给我们.
- 下载后发现下载的内容跟说明不相乎，请到消费记录里找到下载记录反馈给我们，经确认后退回积分.
- 如下载前有疑问，可以通过点击"提供者"的名字，查看对方的联系方式，联系对方咨询.
