Semiparametric inference in generalized mixed effects models

• 成果类型: Article
• 署名作者: Jose Lombardia, Maria; Sperlich, Stefan
• 署名单位: Universidade de Santiago de Compostela; University of Gottingen
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/j.1467-9868.2008.00655.x
• 发表日期: 2008
• 页码: 913-930
• 关键词: quasi-likelihood estimation; small-area estimation; linear-models; prediction; variance; error

摘要

The paper presents a study of the generalized partially linear model including random effects in its linear part. We propose an estimator that combines likelihood approaches for mixed effects models, with kernel methods. Following the methodology of Hardle and co-workers, we introduce a test for the hypothesis of a parametric mixed effects model against the alternative of a semiparametric mixed effects model. The critical values are estimated by using a bootstrap procedure. The asymptotic theory for the methods is provided, as are the results of a simulation study. These verify the feasibility and the excellent behaviour of the methods for samples of even moderate size. The usefulness of the methodology is illustrated with an application in which the objective is to estimate forest coverage in Galicia, Spain.