Focused information criterion and model averaging for generalized additive partial linear models

Details Focused information criterion and model averaging for generalized additive partial linear models Focused information criterion and model averaging for generalized additive partial linear models

2011-03-08

arxiv.org/abs/1103.1480v1

Annals of Statistics 2011, Vol. 39, No. 1, 174-200

Mathematics

Statistics Theory

Published in at http://dx.doi.org/10.1214/10-AOS832 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of

Scientific paper

10.1214/10-AOS832

We study model selection and model averaging in generalized additive partial linear models (GAPLMs). Polynomial spline is used to approximate nonparametric functions. The corresponding estimators of the linear parameters are shown to be asymptotically normal. We then develop a focused information criterion (FIC) and a frequentist model average (FMA) estimator on the basis of the quasi-likelihood principle and examine theoretical properties of the FIC and FMA. The major advantages of the proposed procedures over the existing ones are their computational expediency and theoretical reliability. Simulation experiments have provided evidence of the superiority of the proposed procedures. The approach is further applied to a real-world data example.

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