A01=R.J. Tibshirani
A01=T.J. Hastie
Additive
additive model applications in research
Additive Model Fit
advanced data analysis
Author_R.J. Tibshirani
Author_T.J. Hastie
Backfitting Algorithm
Backfitting Procedure
case study methods
Category=JHBC
Conditional Expectation
Conditional Expectation Operator
Diabetes Data
eq_bestseller
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_society-politics
Equivalent Kernel
Generalized Additive Models
Interior Knot
Kernel Smoothers
Linear Smoothers
Local Likelihood Estimation
Mars
Matched Case Control Data
Multiple Linear Regression
nonparametric regression
Ozone Concentration Data
Pointwise Standard Errors
Proportional Hazards Model
R.J. Tibshirani
Regression Splines
response transformation
Scatterplot Smoothers
Semi-parametric Model
Smoother Matrix
Smoothing Parameter
smoothing splines
Standard Error Bands
statistical modeling
Product details
- ISBN 9780412343902
- Weight: 810g
- Dimensions: 152 x 229mm
- Publication Date: 01 Jun 1990
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Hardback
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This book describes an array of power tools for data analysis that are based on nonparametric regression and smoothing techniques. These methods relax the linear assumption of many standard models and allow analysts to uncover structure in the data that might otherwise have been missed. While McCullagh and Nelder's Generalized Linear Models shows how to extend the usual linear methodology to cover analysis of a range of data types, Generalized Additive Models enhances this methodology even further by incorporating the flexibility of nonparametric regression. Clear prose, exercises in each chapter, and case studies enhance this popular text.
