|Saturday, February 25|
|CS23 Latent Variable and Mixed Effects Models||
Sat, Feb 25, 11:00 AM - 12:30 PM
City Terrace 7
Nonparametric Mixed-Effects Regression for Large Samples (303294)*Nathaniel Erik Helwig, University of Minnesota
Keywords: Nonparametric regression, Mixed-effects, Smoothing
Linear mixed-effects (LME) regression models are a popular approach for analyzing correlated data. Nonparametric extensions of the LME regression model have been proposed, but the heavy computational cost makes these extensions impractical for analyzing large samples. Recent computational advances make it possible to fit nonparametric mixed-effects (NPME) regression models with multiple fixed and/or random effects to large samples of data. In this talk, I (i) give an overview of NPME regression, (ii) discuss three methods—and corresponding R packages—for fitting NPME regression models, and (iii) demonstrate the potential of NPME models using both simulated and real data examples.