Varying-coefficient mean-covariance regression analysis for longitudinal data

DOI10.1016/j.jspi.2014.11.008zbMath1311.62083OpenAlexW1986781971MaRDI QIDQ2344387

Shu Liu, Guodong Li

Publication date: 15 May 2015

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2014.11.008

zbMATH Keywords

longitudinal dataefficiencylocal polynomial regressionmodified Cholesky decompositionvarying-coefficientwithin-subject correlation

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Measures of association (correlation, canonical correlation, etc.) (62H20)

Related Items (13)

Joint estimation for single index mean-covariance models with longitudinal data ⋮ A moving average Cholesky factor model in covariance modeling for composite quantile regression with longitudinal data ⋮ Estimation and model identification of longitudinal data time-varying nonparametric models ⋮ Efficient parameter estimation via modified Cholesky decomposition for quantile regression with longitudinal data ⋮ Robust estimation via modified Cholesky decomposition for modal partially nonlinear models with longitudinal data ⋮ Unnamed Item ⋮ Conditional mix-GEE models for longitudinal data with unspecified random-effects distributions ⋮ Smoothed empirical likelihood inference via the modified Cholesky decomposition for quantile varying coefficient models with longitudinal data ⋮ Estimation of a rank-reduced functional-coefficient panel data model with serial correlation ⋮ Two step estimations for a single-index varying-coefficient model with longitudinal data ⋮ Analysis of longitudinal data with semiparametric varying-coefficient mean-covariance models ⋮ Estimation of semi-varying coefficient models for longitudinal data with irregular error structure ⋮ Quantile estimations via modified Cholesky decomposition for longitudinal single-index models

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