Least Absolute Deviations: Theory, Applications and Algorithms - Tapa blanda

Sinopsis

1. Generalities.- 1.1 Introduction.- 1.2 Historical Background.- 1.3 Some Mathematical Background.- 1.4 Notes.- 2. LAD in Linear Regression.- 2.1 Introduction.- 2.2 Some Statistical Properties of LAD Estimators.- 2.3 Robustness of LAD: Breakdown and Influence.- 2.4 LAD in M and R-Estimation.- 2.5 Sampling Behavior of LAD.- 2.6 Notes.- 3. LAD in Autoregression.- 3.1 Introduction.- 3.2 Behavior of LAD in Autoregressions.- 3.3 Sampling Behavior of LAD.- 3.4 Notes.- 4. LAD in Multi-Way Tables.- 4.1 One-way layout.- 4.2 Two-way layout.- 4.3 Properties of LAD estimates.- 4.4 LAD and Median Polish.- 4.5 Nonuniqueness of "row-pius-column" fits.- 4.6 Notes.- 5. LAD Spline Fitting.- 5.1 Spline Functions.- 5.2 Conditional and Local Quantiles.- 5.3 Quantile Splines.- 5.4 Examples.- 5.5 Notes.- 6. LAD and Linear Programming.- 6.1 Introduction and Background.- 6.2 LP is LAD is LP.- 6.3 Some Complexity Questions.- 6.4 Dense LP Problems as LAD Fits.- 6.5 Notes.- 7. Algorithms for LAD.- 7.1 Introduction and Background.- 7.2 Three Special Purpose LAD Algorithms.- 7.2.1 The Barrodale-Roberts Algorithm.- 7.2.2 The Bartels-Conn-Sinclair Algorithm.- 7.2.3 The Bloomfield-Steiger Algorithm.- 7.2.4 Summary.- 7.3 The Three Algorithms Compared.- 7.4 Slightly Overdetermined Equation Systems.- 7.5 Other Methods.- 7.6 Notes.- 8. Appendix.- Index of Names.- Index of Subjects.

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