TY - JOUR
AU - Raynor, William J.
AB -  Abstract We consider the penalized likelihood method for estimating nonparametric regression functions in generalized linear models (Nelder and Wedderburn 1972) and present a generalized cross-validation procedure for empirically assessing an appropriate amount of smoothing in these estimates. Asymptotic arguments and numerical simulations are used to show that the generalized cross-validatory procedure preforms well from the point of view of a weighted mean squared error criterion. The methodology adds to the battery of graphical tools for model building and checking within the generalized linear model framework. Included are two examples motivated by medical and horticultural applications. 
TI - Automatic Smoothing of Regression Functions in Generalized Linear Models
JF - Journal of the American Statistical Association
DO - 10.1080/01621459.1986.10478243
DA - 1986-03-01
UR - https://www.deepdyve.com/lp/taylor-francis/automatic-smoothing-of-regression-functions-in-generalized-linear-johbnjlS42
SP - 96
EP - 103
VL - 81
IS - 393
DP - DeepDyve
ER -