Theory and Applications of Recent Robust Methods


ISBN: 978-3-0348-9636-8 (Print) 978-3-0348-7958-3 (Online)

Table of contents (34 chapters)

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  1. Front Matter

    Pages i-xi

  2. Chapter

    Pages 1-12

    Bias Behavior of the Minimum Volume Ellipsoid Estimate

  3. Chapter

    Pages 13-25

    A Study of Belgian Inflation, Relative Prices and Nominal Rigidities using New Robust Measures of Skewness and Tail Weight

  4. Chapter

    Pages 27-37

    Robust Strategies for Quantitative Investment Management

  5. Chapter

    Pages 39-48

    An Adaptive Algorithm for Quantile Regression

  6. Chapter

    Pages 49-58

    On Properties of Support Vector Machines for Pattern Recognition in Finite Samples

  7. Chapter

    Pages 59-70

    Smoothed Local L-Estimation With an Application

  8. Chapter

    Pages 71-82

    Fast Algorithms for Computing High Breakdown Covariance Matrices with Missing Data

  9. Chapter

    Pages 83-91

    Generalized d-fullness Technique for Breakdown Point Study of the Trimmed Likelihood Estimator with Application

  10. Chapter

    Pages 93-104

    On Robustness to Outliers of Parametric L 2 Estimate Criterion in the Case of Bivariate Normal Mixtures: a Simulation Study

  11. Chapter

    Pages 105-117

    Robust PCR and Robust PLSR: a Comparative Study

  12. Chapter

    Pages 119-130

    Analytic Estimator Densities for Common Parameters under Misspecified Models

  13. Chapter

    Pages 131-140

    Empirical Comparison of the Classification Performance of Robust Linear and Quadratic Discriminant Analysis

  14. Chapter

    Pages 141-152

    Estimates of the Tail Index Based on Nonparametric Tests

  15. Chapter

    Pages 153-164

    On Mardia’s Tests of Multinormality

  16. Chapter

    Pages 165-171

    Robustness in Sequential Discrimination of Markov Chains under “Contamination”

  17. Chapter

    Pages 173-182

    Robust Box-Cox Transformations for Simple Regression

  18. Chapter

    Pages 183-194

    Consistency of the Least Weighted Squares Regression Estimator

  19. Chapter

    Pages 195-206

    Algorithms for Robust Model Selection in Linear Regression

  20. Chapter

    Pages 207-219

    Analyzing the Number of Samples Required for an Approximate Monte-Carlo LMS Line Estimator

  21. Chapter

    Pages 221-233

    Visualizing 1D Regression

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