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Nonparametric Statistics and Mixture Models cover

This festschrift includes papers authored by many collaborators, colleagues, and students of Professor Thomas P Hettmansperger, who worked in research in nonparametric statistics, rank statistics, robustness, and mixture models during a career that spanned nearly 40 years. It is a broad sample of peer-reviewed, cutting-edge research related to nonparametrics and mixture models.

Sample Chapter(s)
Estimation of Location and Scale Parameters Based on Kernel Functional Estimators (239 KB)


Contents:
  • Estimation of Location and Scale Parameters Based on Kernel Functional Estimators (I A Ahmad & M Amezziane)
  • Dealing with More Variables Than the Sample Size: An Application to Shape Analysis (C Brombin et al.)
  • Statistical Models for Globular Cluster Luminosity Distribution (M-L G Buot & D St P Richards)
  • Shock Models for Defaults: Parametric and Nonparametric Approaches (P Cirillo & J Hüsler)
  • On the Non-Gaussian Asymptotics of the Likelihood Ratio Test Statistic for Homogeneity of Covariance (M Hallin)
  • Recent History Functional Linear Models (K Kim et al.)
  • QQ Plots for Assessing Symmetry Models (J I Marden)
  • Estimation of Hazard Functions with Shape Restrictions Using Regression Splines (M Meyer & D Habtzghi)
  • An Empirical Study of Indirect Cross-Validation (O Savchuk et al.)
  • Rank Regression Under Possible Model Misspecification (L Wang)
  • and other papers

Readership: Graduate students and researchers in statistics.