Optimal smoothing in nonparametric mixed-effect models
70 citations recorded by the source corpus.
Mixed-effect models are widely used for the analysis of correlated data such as longitudinal data and repeated measures. In this article, we study an approach to the nonparametric estimation of mixed-effect models. We consider models with parametric random effects and flexible fixed effects, and employ the penalized least squares method to estimate the models. The issue to be addressed is the selection of smoothing parameters through the generalized cross-validation method, which is shown to yield optimal smoothing for both real and latent random effects. Simulation studies are conducted to investigate the empirical performance of generalized cross-validation in the context. Real-data examples are presented to demonstrate the applications of the methodology.
Subfields: Economics and Econometrics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/5fc145e920dca244172d4f459a5d81380e099cf1
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
In Statistics and Probability
Citations in the corpus
- Asymptotic optimality and efficient computation of the leave-subject-out cross-validation (2012, background): “In two related papers, Gu and Ma (2005) and Han and Gu (2008) developed modifications of the GCV for dependent data under assumptions on the correlation structure and established the optimality of the modified GCVs.”