Title: Empirical likelihood inference for generalized additive partially linear models

Abstract:

Generalized additive partially linear models enjoy the simplicity of GLMs and the flexibility of GAMs because they combine both parametric and nonparametric components. Based on spline-backfitted kernel estimator, we propose empirical likelihood (EL)-based pointwise confidence intervals and simultaneous confidence bands (SCBs) for the nonparametric component functions to make statistical inference. Simulation study strongly supports the asymptotic theory and shows that EL-based SCBs are much easier for implementation and have better performance than Wald-type SCBs. We apply the proposed method to a credit rating study and provide SCBs for the effect of the financial ratios on the default probabilities.

Bio:

Professor Rong Liu received his Ph.D degree in Statistics from Michigan State University. Currently he is an Associate Professor in the Department of Mathematics and Statistics at The University of Toledo. His research interests are spline and kernel smoothing, dimension reduction, statistical computing, credit prediction and financial econometrics.