Nonparametric Regression and Generalized Linear Models
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A01=Bernard. W. Silverman
A01=P.J. Green
advanced nonparametric regression methods
applied mathematics
approach
Author_Bernard. W. Silverman
Author_P.J. Green
Band Matrix
Category=PBF
Category=PBT
Category=PBWH
computational statistics
Cross-validation Score
Cubic Spline
Cubic Spline Smoothing
Data Sets
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
Finite Window
GCV Score
Generalize Linear Model
Generalized Cross-validation
GLMs
graduate level statistics
Hat Matrix
Linear Model
Multiple Linear Regression
Natural Cubic Spline
Nonlinear Functional
partial
Partial Splines
Penalized Log Likelihood
penalty
regression modeling techniques
residual
roughness
Roughness Penalty
Roughness Penalty Approach
Semiparametric Model
smoother
smoothing algorithms
Smoothing Parameter
spline
squares
statistical data analysis
sum
Tensor Product Splines
thin
Thin Plate Spline Methods
Thin Plate Spline Smoother
Triceps Skinfold
Product details
- ISBN 9780412300400
- Weight: 840g
- Dimensions: 152 x 229mm
- Publication Date: 01 May 1993
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Hardback
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In recent years, there has been a great deal of interest and activity in the general area of nonparametric smoothing in statistics. This monograph concentrates on the roughness penalty method and shows how this technique provides a unifying approach to a wide range of smoothing problems. The method allows parametric assumptions to be realized in regression problems, in those approached by generalized linear modelling, and in many other contexts.
The emphasis throughout is methodological rather than theoretical, and it concentrates on statistical and computation issues. Real data examples are used to illustrate the various methods and to compare them with standard parametric approaches. Some publicly available software is also discussed. The mathematical treatment is self-contained and depends mainly on simple linear algebra and calculus.
This monograph will be useful both as a reference work for research and applied statisticians and as a text for graduate students and other encountering the material for the first time.
P.J. Green, Bristol Univesity. Bernard. W. Silverman St. Peters College, Oxford.
