Smoothing with Mixed Model Software

Smoothing sample extremes: The mixed model approach

Model Selection in Linear Mixed Models Using Mdl Criterion with an Application to Spline Smoothing

For spline smoothing one can rewrite the smooth estimation as a linear mixed model (LMM) where the smoothing parameter appears as the variance of spline basis coefficients. Smoothing methods that use basis functions with penalization can utilize maximum likelihood (ML) theory in LMM framework ([8]). We introduce the minimum description length (MDL) model selection criterion in LMM and propose a...

SUGI 28: Smoothing with SAS(r) PROC MIXED

Mixed models are an extension of regression models that allows for incorporation of random effects. The application of mixed-effects models to practical data analysis has greatly expanded with consequent development of theory and computer software. It also turns out that mixed models are closely related to smoothing. Nonparametric regression models, especially the general smoothing spline model...

Optimal Smoothing in Nonparametric Mixed-Effect Models

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 s...

Mixed-Model Assembly Line Balancing with Considering Reliability

This paper presents a multi-objective simulated annealing algorithm for the mixed-model assembly line balancing with stochastic processing times. Since, the stochastic task times may have effects on the bottlenecks of a system, maximizing the weighted line efficiency (equivalent to the minimizing the number of station), minimizing the weighted smoothness index and maximizing the system reliabil...