Local Polynomial Modelling and Its Applications
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A01=Irene Gijbels
A01=Jianqing Fan
advanced nonparametric regression applications
Author_Irene Gijbels
Author_Jianqing Fan
automatic model selection
Average Derivative Estimation
bandwidth
Bandwidth Selection Procedures
Bandwidth Selection Rule
Bandwidth Selector
Category=PBF
Category=PBT
Category=PBWH
Data Set
Discrete Wavelet Coefficients
Effective Dimension Reduction
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
fit
fitting
Gam
high-dimensional statistics
I. Gijbels
J. Fan
linear
Local Likelihood Estimator
Local Likelihood Method
Local Linear Fit
Local Linear Regression Estimator
Local Log Likelihood
Local Polynomial
Local Polynomial Fitting
Local Polynomial Regression Estimators
Motorcycle Data
Multivariate Adaptive Regression Splines
multivariate data analysis
Nadaraya Watson Estimator
nonparametric regression methods
parameter
Pilot Bandwidth
regression
Scatter Plot Smoothing
selection
selector
Single Index Models
Sliced Inverse Regression
smoothing
Smoothing Parameter
smoothing techniques
Spectral Density Estimation
splines
time series modeling
Product details
- ISBN 9780412983214
- Weight: 830g
- Dimensions: 152 x 229mm
- Publication Date: 01 Mar 1996
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Hardback
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Data-analytic approaches to regression problems, arising from many scientific disciplines are described in this book. The aim of these nonparametric methods is to relax assumptions on the form of a regression function and to let data search for a suitable function that describes the data well. The use of these nonparametric functions with parametric techniques can yield very powerful data analysis tools. Local polynomial modeling and its applications provides an up-to-date picture on state-of-the-art nonparametric regression techniques. The emphasis of the book is on methodologies rather than on theory, with a particular focus on applications of nonparametric techniques to various statistical problems. High-dimensional data-analytic tools are presented, and the book includes a variety of examples. This will be a valuable reference for research and applied statisticians, and will serve as a textbook for graduate students and others interested in nonparametric regression.
