Impact of unknown covariance structures in semiparametric models for longitudinal data: an application to Wisconsin diabetes data

DOI10.1016/j.csda.2009.05.008zbMath1453.62135OpenAlexW2042682122MaRDI QIDQ961915

Yingcun Xia, Mari Palta, Anoop Shankar, Jia-Liang Li

Publication date: 1 April 2010

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2009.05.008

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (5)

Analysing nonlinear time series with central subspace ⋮ Efficient semiparametric estimation via Cholesky decomposition for longitudinal data ⋮ Semiparametric model average prediction in panel data analysis ⋮ Multiple-index approach to multiple autoregressive time series model ⋮ Bayesian estimation of varying-coefficient models with missing data, with application to the Singapore Longitudinal Aging Study

Uses Software

• S-PLUS
• MEMSS

Cites Work

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• New Estimation and Model Selection Procedures for Semiparametric Modeling in Longitudinal Data Analysis
• Efficient Semiparametric Marginal Estimation for Longitudinal/Clustered Data
• Functional Data Analysis for Sparse Longitudinal Data

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