Estimation of covariance matrix of multivariate longitudinal data using modified Choleksky and hypersphere decompositions-Reference-Cited by-同舟云学术

Estimation of covariance matrix of multivariate longitudinal data using modified Choleksky and hypersphere decompositions

Published:2019-09-12 Issue:1 Volume:76 Page:75-86
ISSN:0006-341X
Container-title:Biometrics
language:en
Short-container-title:Biometrics

Author:

Lee Keunbaik¹^ORCID,Cho Hyunsoon²^ORCID,Kwak Min‐Sun³^ORCID,Jang Eun Jin⁴^ORCID

Affiliation:

1. Department of StatisticsSungkyunkwan UniversitySeoul South Korea

2. Department of Cancer Control and Population HealthGraduate School of Cancer Science and Policy, National Cancer CenterGoyang South Korea

3. Department of Internal Medicine, Healthcare System Gangnam CenterHealthcare Research Institute, Seoul National University HospitalSeoul South Korea

4. Department of Information StatisticsAndong National UniversityAndong South Korea

Funder

National Research Foundation of Korea

Publisher

Wiley

Subject

Applied Mathematics,General Agricultural and Biological Sciences,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine,Statistics and Probability

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/biom.13113

Reference19 articles.

1. Asymptotically Efficient Estimation of Covariance Matrices with Linear Structure

2. The Matrix-Logarithmic Covariance Model

3. Daniels M. J.andHogan J. W.(2008)Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis. Chapman & Hall/CRC.

4. A new nested Cholesky decomposition and estimation for the covariance matrix of bivariate longitudinal data

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