Linear mixed function‐on‐function regression models
Copyright policy )Linear mixed function‐on‐function regression models
SummaryWe 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.”
- Children's Hospital of Philadelphia United States
linear mixed effects models, Generalized linear models (logistic models), Likelihood Functions, principal component analysis, Numerical Analysis, Computer-Assisted, Factor analysis and principal components; correspondence analysis, Models, Biological, Applications of statistics to biology and medical sciences; meta analysis, Pattern Recognition, Automated, Data Interpretation, Statistical, Linear Models, Regression Analysis, Computer Simulation, Nonparametric regression and quantile regression, ECME algorithm, Algorithms, functional data analysis
linear mixed effects models, Generalized linear models (logistic models), Likelihood Functions, principal component analysis, Numerical Analysis, Computer-Assisted, Factor analysis and principal components; correspondence analysis, Models, Biological, Applications of statistics to biology and medical sciences; meta analysis, Pattern Recognition, Automated, Data Interpretation, Statistical, Linear Models, Regression Analysis, Computer Simulation, Nonparametric regression and quantile regression, ECME algorithm, Algorithms, functional data analysis
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).18 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!