Adaptive Regression

• 1 Prologue.- 1.1 Introduction.- 1.2 Adaptive Combination of Estimators.- 1.3 Notes.- 2 Regression Methods.- 2.1 Introduction.- 2.2 LS Regression.- 2.3 Ridge Regression.- 2.4 LAD Regression.- 2.5 M-Regression.- 2.6 L-Regression.- 2.7 Other Regression Estimators.- 2.8 Estimators of Scale Parameter.- 2.9 Notes.- 3 Adaptive LAD + LS Regression.- 3.1 Introduction.- 3.2 Convex Combination of LAD and LS Regressions.- 3.3 Adaptive Combination of LAD and LS Regressions.- 3.4 Illustrative Examples.- 3.5 Notes.- 4 Adaptive LAD + TLS Regression.- 4.1 Introduction.- 4.2 Adaptive Combination of LAD and Trimmed LS.- 4.3 An Example of Multiple Regression.- 4.4 Notes.- 5 Adaptive LAD + M-Regression.- 5.1 Introduction.- 5.2 Combination of LAD and M-Estimators.- 5.3 Adaptive Combination of LAD and M-Estimators.- 5.4 An Example of Multiple Regression.- 5.5 Notes.- 6 Adaptive LS + TLS Regression.- 6.1 Introduction.- 6.2 Adaptive Combination of Mean and Trimmed Mean.- 6.3 Adaptive Combination of LS and TLS Regressions.- 6.4 Example of Multiple Regression.- 6.5 Notes.- 7 Adaptive Choice of Trimming.- 7.1 Introduction.- 7.2 Fully Adaptive Trimmed Mean and TLS.- 7.3 Adaptive Choice for fhe Trimmed Mean.- 7.4 Adaptive Choice in Linear Model Based on Ranks.- 7.5 Adaptive Choice in Linear Model Based on Regression Rank Scores.- 7.6 Notes.- 8 Adaptive Combination of Tests.- 8.1 Introduction.- 8.2 Types of Tests.- 8.3 Adaptive Combination of F-Test and Median-Type Test.- 8.4 Adaptive Combination of M-Test and Median-Type Test.- 8.5 Notes.- 9 Computational Aspects.- 9.1 Introduction.- 9.2 Computing the Adaptive Combination of LS and LAD.- 9.3 Program ADAPTIVE.- 10 Some Asymptotic Results.- 10.1 Asymptotic Properties of Studentized M-Estimators.- 10.2 Uniform Asymptotic Linearity of M-Statistics.- 10.3 Estimators of Scale Parameter.- 10.4 Optimal Choice of ?n.- 11 Epilogue.- References.- Author Index.

From the reviews: MATHEMATICAL REVIEWS "Despite its high level, the book is extremely readable and gives new insight into the problem of estimation in the linear regression model."