A01=Alan Miller
Accurate Digits
advanced variable selection strategies
Author_Alan Miller
Backward Elimination
Bayes Factors
Bayesian regression analysis
bootstrapping in statistics
Category=PBT
cholesky
Cholesky Factorization
Competition Bias
Conditional Maximum Likelihood Method
cross-validation methods
Decimal Digits
Empirical Orthogonal Functions
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
exhaustive
factorization
forward
Forward Selection
Gauss Jordan Method
Hamiltonian Cycle
linear model bias correction
LS Regression
Model Mj
model selection techniques
Planar Rotations
Posterior Probabilities
predictor
Press Statistic
Regression Sums
residual
Residual Standard Deviation
Residual Sum
ridge
Ridge Regression
search
Steam
stochastic subset algorithms
Subset Selection
sum
Tecator Data
Triangular Factorizations
True Residuals
variables
Product details
- ISBN 9780367396220
- Weight: 453g
- Dimensions: 152 x 229mm
- Publication Date: 05 Sep 2019
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Paperback
Secure
checkout
Fast Shipping
Easy returns
Originally published in 1990, the first edition of Subset Selection in Regression filled a significant gap in the literature, and its critical and popular success has continued for more than a decade. Thoroughly revised to reflect progress in theory, methods, and computing power, the second edition promises to continue that tradition. The author has thoroughly updated each chapter, incorporated new material on recent developments, and included more examples and references.
New in the Second Edition:
Subset Selection in Regression, Second Edition remains dedicated to the techniques for fitting and choosing models that are linear in their parameters and to understanding and correcting the bias introduced by selecting a model that fits only slightly better than others. The presentation is clear, concise, and belongs on the shelf of anyone researching, using, or teaching subset selecting techniques.
Miller, Alan
