jmcm: a Python package for analyzing longitudinal data using joint mean-covariance models

DOI10.1080/03610918.2021.1990324OpenAlexW3207571415WikidataQ113279019 ScholiaQ113279019MaRDI QIDQ6181892

Jian-Xin Pan, Xuerui Yang

Publication date: 23 January 2024

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2021.1990324

zbMATH Keywords

Pythonhypothesis testsjoint mean-covariance modelslikelihood estimationsmodified Cholesky decompositions

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Measures of association (correlation, canonical correlation, etc.) (62H20)

Cites Work

• Unnamed Item
• Longitudinal data analysis using generalized linear models
• Asymptotically efficient estimation of covariance matrices with linear structure
• Modelling of covariance structures in generalised estimating equations for longitudinal data
• Random Effects Selection in Linear Mixed Models
• On modelling mean-covariance structures in longitudinal studies
• Maximum likelihood estimation of generalised linear models for multivariate normal covariance matrix
• Nonparametric Estimation of Covariance Structure in Longitudinal Data
• Joint mean-covariance models with applications to longitudinal data: unconstrained parameterisation
• Regression models for covariance structures in longitudinal studies
• A Joint Modelling Approach for Longitudinal Studies

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