The advent of high-speed, affordable computers in the last two decades has given a new boost to the nonparametric way of thinking. Classical nonparametric procedures, such as function smoothing, suddenly lost their abstract flavour as they became practically implementable. In addition, many previously unthinkable possibilities became mainstream; prime examples include the bootstrap and resampling methods, wavelets and nonlinear smoothers, graphical methods, data mining, bioinformatics, as well as the more recent algorithmic approaches such as bagging and boosting. This volume is a collection of short articles - most of which having a review component - describing the state-of-the art of Nonparametric Statistics at the beginning of a new millennium.

Key features:

• algorithic approaches
• wavelets and nonlinear smoothers
• graphical methods and data mining
• biostatistics and bioinformatics
• bagging and boosting
• support vector machines
• resampling methods


Researchers in statistics; researchers in machine learning.

Table of Contents


1. Algorithmic Approaches to Statistics

An introduction to support vector machines (B. Schölkopf).
Bagging, subagging and bragging for improving some prediction algorithms (P. Bühlmann).
Data compression by geometric quantization (Nkem-Amin Khumbah , E. J. Wegman).

2. Functional Data Analysis

Functional data analysis in evolutionary biology (N. E. Heckman).
Functional nonparametric statistics: a double infinite dimensional framework (F. Ferraty, P. Vieu).

3. Nonparametric Model Building

Nonparametric models for ANOVA and ANCOVA: a review (M. G. Akritas, E. Brunner).
Isotonic additive interaction models (I. Gluhovsky).
A nonparametric alternative to analysis of covariance (A. Bathke, E. Brunner).

4. Goodness Of Fit

Assessing structural relationships between distributions - a quantile process approach based on Mallows distance (G. Freitag, A. Munk, M. Vogt).
Almost sure representations in survival analysis under censoring and truncation: applications to goodness-of-fit tests (R. Cao, W. González Manteiga, C. Iglesias Pérez)



No. of pages:
© 2003
JAI Press
eBook ISBN:
Print ISBN:

About the authors

M.G. Akritas

Affiliations and Expertise

Penn State University, Department of Statistics, PA, USA

D.N. Politis

Affiliations and Expertise

The University of California, Department of Mathematics, La Jolla, USA


"The book provides current literature in many areas." Tapabrata Maiti (Iowa State University), in: Journal of American Statistical Association, 2005