By
M.G. Akritas, Penn State University, Department of Statistics, PA, USA
D.N. Politis, The University of California, Department of Mathematics, La Jolla, USA
Description
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
Audience:
Researchers in statistics; researchers in machine learning.
