TY - JOUR
T1 - Nonparametric models and methods for ancova with dependent data
AU - Tsangari, Haritini
AU - Akritas, Michael G.
N1 - Funding Information:
The authors are grateful to Dr. Vernon Chinchilli, Professor of Health Evaluation Sciences and Statistics, Penn State University, for providing the cholesterol data. This research was supported by NSF grant SES-0318200.
PY - 2004
Y1 - 2004
N2 - The nonparametric ANCOVA model of Akritas et al. [Akritas, M. G., Arnold, S. F. and Du, Y. (2000). Nonparametric models and methods for nonlinear analysis of covariance. Biometrika, 87(3), 507-526.] is extended to longitudinal data and for up to three covariates. In this model the response distributions need not be continuous or to comply to any parametric or semiparainetric model. The nonparametric covariate effect can be different in different factor level combinations. Nonparametric hypotheses of no main factor effects, no interaction and no simple effect, which adjust for the covariate values, are considered. The test statistics, which are based on averages over the covariate values of certain Nadaraya-Watson regression quantities, have asymptotically a central chi-squared distribution under their respective null hypotheses. Small sample corrections to the asymptotic distribution are provided. Simulation results and data analysis for a real dataset are presented.
AB - The nonparametric ANCOVA model of Akritas et al. [Akritas, M. G., Arnold, S. F. and Du, Y. (2000). Nonparametric models and methods for nonlinear analysis of covariance. Biometrika, 87(3), 507-526.] is extended to longitudinal data and for up to three covariates. In this model the response distributions need not be continuous or to comply to any parametric or semiparainetric model. The nonparametric covariate effect can be different in different factor level combinations. Nonparametric hypotheses of no main factor effects, no interaction and no simple effect, which adjust for the covariate values, are considered. The test statistics, which are based on averages over the covariate values of certain Nadaraya-Watson regression quantities, have asymptotically a central chi-squared distribution under their respective null hypotheses. Small sample corrections to the asymptotic distribution are provided. Simulation results and data analysis for a real dataset are presented.
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U2 - 10.1080/10485250310001624792
DO - 10.1080/10485250310001624792
M3 - Article
AN - SCOPUS:2542559652
SN - 1048-5252
VL - 16
SP - 403
EP - 420
JO - Journal of Nonparametric Statistics
JF - Journal of Nonparametric Statistics
IS - 3-4
ER -