title: Linear mixed function-on-function regression models.
abstract: We develop a linear mixed regression model where both the response and the predictor are functions. Model parameters are estimated by maximizing the log likelihood via the ECME algorithm. The estimated variance parameters or covariance matrices are shown to be positive or positive definite at each iteration. In simulation studies, the approach outperforms in terms of the fitting error and the MSE of estimating the "regression coefficients."
authors: Center for Outcomes Research, Children's Hospital of Philadelphia, Civic Center Blvd, Philadelphia, Pennsylvania 19104, U.S.A.
authors: Wei
authors: W
authors: Wang
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D016013:Likelihood Functions; D016014:Linear Models; D008954:Models, Biological; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods; D012044:Regression Analysis
keywords: ECME algorithm; Functional data analysis; Linear mixed effects models; Principal component analysis