@article{8b41f62d6e1b4d2caf00b14f74fc641b,
title = "Generalised partial linear single-index mixed models for repeated measures data",
abstract = "In this paper, we propose generalised partial linear single-index mixed models for analysing repeated measures data.A penalised quasi-likelihood approach using P-spline is used to estimate the nonparametric function, linear parameters, and single-index coefficients. Asymptotic properties of the estimators are developed when the dimension of spline basis grows with increasing sample size. Simulation examples and two applications: the study of health effects of air pollution in North Carolina, and treatment effect of naltrexone on health costs for alcohol-dependent individuals, illustrate the effectiveness of our approach.",
keywords = "Asymptotics, Penalised splines, Random effects, Smoothing parameter selection",
author = "Jinsong Chen and Inyoung Kim and Terrell, {George R.} and Lei Liu",
note = "Funding Information: The authors gratefully acknowledge the support from the International Institute of Applied System Analysis, especially the Atmospheric Pollution and Economic Development program. The data of air pollutants were obtained from US Environmental Protection Agency (EPA). The mortality data were provided by the Odum Institute, University of North Carolina. The climate data were supplied by the State Climate Office of North Carolina. The health cost data were kindly provided by Dr Gary Zarkin. We are grateful to Mr Arnie Aldridge for his help of data analysis. We thank two anonymous referees for their careful reading of our paper, incisive comments and suggestions for improvement. Dr Lei Liu{\textquoteright}s work is supported by AHRQ R01 HS 020263. Dr Inyoung Kim{\textquoteright}s study was supported by a grant from both National Science Foundation (CNS-096480) and National Science Foundation (CNS-1115839). Dr Lei Liu{\textquoteright}s work is supported by AHRQ R01 HS 020263. Publisher Copyright: {\textcopyright} American Statistical Association and Taylor & Francis 2014.",
year = "2014",
month = mar,
day = "10",
doi = "10.1080/10485252.2014.891029",
language = "English",
volume = "26",
pages = "291--303",
journal = "Journal of Nonparametric Statistics",
issn = "1048-5252",
number = "2",
}