Local Likelihood Estimation in Generalized Additive Models

Kauermann, Göran and Opsomer, J. D. (2000): Local Likelihood Estimation in Generalized Additive Models. Collaborative Research Center 386, Discussion Paper 190

Abstract

Generalized additive models are a popular class of multivariate nonparametric regression models, due in large part to the ease of use of the local scoring estimation algorithm. However, the theoretical properties of the local scoring estimator are poorly understood. In this article, we propose a local likelihood estimator for generalized additive models that is closely related to the local scoring estimator fitted by local polynomial regression. We derive the statistical properties of the estimator and show that it achieves the same asymptotic convergence rate as a one-dimensional local polynomial regression estimator. We also propose a wild bootstrap estimator for calculating pointwise confidence intervals for the additive component functions. The practical behavior of the proposed estimator is illustrated through simulation experiments and an example.

• Item Type: Paper
• Keywords: backfitting, bootstrapping, generalized additive models, local likelihood, local polynomial regression, local scoring, wild bootstrap
• Faculties: Mathematics, Computer Science and Statistics > Statistics > Collaborative Research Center 386; Special Research Fields > Special Research Field 386
• Subjects: 500 Science > 510 Mathematics
• Language: English