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| RECENT ADVANCES AND TRENDS IN NONPARAMETRIC STATISTICS
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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.
Contents
Preface.
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)
5. High-Dimensional Data And Visualization
Data depth:
center-outward ordering of multivariate data and nonparametric multivariate statistics
(R. Y. Liu).
Visual exploration of data
through their graph representations
(G. Michailidis).
6. Nonparametric Regression
Inference for nonsmooth regression curves
and surfaces using kernel-based methods
(I. Gijbels).
Nonparametric smoothing methods for a class of non-standard curve
estimation
problems
(O. Linton, E. Mammen).
Weighted local linear approach to censored nonparametric regression
(Z. Cai).
7. Topics
In Nonparametrics
Adaptive quantile regression
(S. van de Geer).
Set estimation: an overview and some recent developments
(A. Cuevas, A. Rodríguez-Casal).
Nonparametric methods for heavy tailed vector data: a survey with applications
from finance
and hydrology
(M. M. Meerschaert, Hans-Peter Scheffler).
8. Nonparametrics in Finance
Nonparametric methods in continuous-time
finance: a selective review
(Z. Cai, Y. Hong).
Nonparametric estimation in a stochastic volatility model
(J. Franke, W. Härdle,
Jens-Peter Kreiss).
Dynamic nonparametric filtering with application to volatility estimation
(Ming-Yen Cheng, J. Fan, V. Spokoiny).
A normalizing and variance-stabilizing transformation for financial time series
(D. N. Politis).
9. Bioinformatics and
Biostatistics
Biostochastics and nonparametrics: oranges and apples?
(P. K. Sen).
Some issues concerning length-biased sampling
in survival analysis
(M. Asgharian, D. B. Wolfson).
Covariate centering and scaling in varying-coefficient regression with
application
to longitudinal growth studies
(C. O. Wu, K. F. Yu, V. W.S. Yuan).
Directed peeling and covering of patient rules
(M. LeBlanc,
J. Moon, J. Crowley).
10. Resampling and Subsampling
Statistical analysis of survival models with Bayesian bootstrap
(J. Lee,
Y. Kim).
On optimal variance estimation under different spatial subsampling schemes
(D. J. Nordman, S. N. Lahiri).
Locally
stationary processes and the local block bootstrap
(A. Dowla, E. Paparoditis, D. N. Politis).
11. Time Series and Stochastic
Processes
Spectral analysis and a class of nonstationary processes
(M. Rosenblatt).
Curve estimation for locally stationary
time series models
(R. Dahlhaus).
Assessing spatial isotropy
(M. Sherman, Y. Guan, J. A. Calvin).
12. Wavelet and Multiresolution
Methods
Automatic landmark registration of 1D curves
(J. Bigot).
Stochastic multiresolution models for turbulence
(B. Whitcher,
J.B. Weiss, D.W. Nychka, T.J. Hoar).
List of Contributors.
| Bibliographic details |
Hardbound, 522 pages, publication date: OCT-2003
ISBN-13: 978-0-444-51378-6
ISBN-10: 0-444-51378-7
Imprint: JAI
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