Statistical Models in S
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€248.00
advanced statistical computing applications
Anne E. Freeny
Backfitting Algorithm
Category=PBT
Category=UFM
computational statistics
Daryl Pregibon
data
data analysis techniques
Data Frame
Dependence Panels
Deviance Residuals
Douglas M. Bates
Dummy Variables
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Eric Grosse
experimental design methods
frame
Generalized Additive Models
Generalized Linear Models
Glm Object
IRLS Algorithm
John M. Chambers
Linda A. Clark
Lm Object
Local Regression Models
Main Effects Model
Misclassification Error Rate
Missing Values
Model Formula
Model Matrix
Negative Log Likelihood
nonlinear modeling
Numeric Predictors
object-oriented programming S
Pointwise Standard Errors
regression modeling
Residual Standard Error
Richard M. Heiberger
Scatterplot Smoother
Smooth Term
Smoothing Parameter
Standard Error Bands
Terminal Nodes
Trevor J. Hastie
William M. Shyu
William S. Cleveland
Product details
- ISBN 9780412830402
- Weight: 1310g
- Dimensions: 156 x 234mm
- Publication Date: 01 Oct 1991
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Hardback
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Statistical Models in S extends the S language to fit and analyze a variety of statistical models, including analysis of variance, generalized linear models, additive models, local regression, and tree-based models. The contributions of the ten authors-most of whom work in the statistics research department at AT&T Bell Laboratories-represent results of research in both the computational and statistical aspects of modeling data.
