Bayesian analysis of latent Markov models with non-ignorable missing data

被引：2

作者：

Cai, Jingheng ^{[1
]}

Liang, Zhibin ^{[1
]}

Sun, Rongqian ^{[1
]}

Liang, Chenyi ^{[1
]}

Pan, Junhao ^{[2
]}

机构：

[1] Sun Yat Sen Univ, Dept Stat, Guangzhou, Guangdong, Peoples R China

[2] Sun Yat Sen Univ, Dept Psychol, Guangzhou, Guangdong, Peoples R China

来源：

JOURNAL OF APPLIED STATISTICS | 2019年 / 46卷 / 13期

基金：

中国国家自然科学基金;

关键词：

Latent Markov models; non-ignorable missing data; MCMC methods; complete DIC; STRUCTURAL EQUATION MODELS; GROWTH MIXTURE-MODELS; VARIABLE MODELS; DISTRIBUTIONS; INFERENCE; RESPONSES;

D O I：

10.1080/02664763.2019.1584162

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Latent Markov models (LMMs) are widely used in the analysis of heterogeneous longitudinal data. However, most existing LMMs are developed in fully observed data without missing entries. The main objective of this study is to develop a Bayesian approach for analyzing the LMMs with non-ignorable missing data. Bayesian methods for estimation and model comparison are discussed. The empirical performance of the proposed methodology is evaluated through simulation studies. An application to a data set derived from National Longitudinal Survey of Youth 1997 is presented.

引用

页码：2299 / 2313

页数：15

共 50 条

[1] Bayesian joint analysis using a semiparametric latent variable model with non-ignorable missing covariates for CHNS data
Ma, Zhihua
Chen, Guanghui
STATISTICAL MODELLING, 2021, 21 (04) : 313 - 331
[2] Bayesian Sensitivity Analysis for Non-ignorable Missing Data in Longitudinal Studies
Li, Tian
Somers, Julian M.
Hu, Xiaoqiong J.
McCandless, Lawrence C.
STATISTICS IN BIOSCIENCES, 2019, 11 (01) : 184 - 205
[3] A SIMPLE AND EFFICIENT ESTIMATION METHOD FOR MODELS WITH NON-IGNORABLE MISSING DATA
Ai, Chunrong
Linton, Oliver
Zhang, Zheng
STATISTICA SINICA, 2020, 30 (04) : 1949 - 1970
[4] Bayesian adaptive lasso quantile regression with non-ignorable missing responses
Chen, Ranran
Dao, Mai
Ye, Keying
Wang, Min
COMPUTATIONAL STATISTICS, 2025, 40 (03) : 1643 - 1682
[5] Empirical likelihood method for non-ignorable missing data problems
Guan, Zhong
Qin, Jing
LIFETIME DATA ANALYSIS, 2017, 23 (01) : 113 - 135
[6] Bayesian analysis of non-linear structural equation models with non-ignorable missing outcomes from reproductive dispersion models
Tang, Nian-Sheng
Chen, Xing
Fu, Ying-Zi
JOURNAL OF MULTIVARIATE ANALYSIS, 2009, 100 (09) : 2031 - 2043
[7] Bayesian sensitivity analysis to the non-ignorable missing cause of failure for hybrid censored competing risks data
Azizi, Fariba
Mahabadi, Samaneh Eftekhari
Omshi, Elham Mosayebi
JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, 2020, 90 (12) : 2228 - 2255
[8] A Bayesian algorithm based on auxiliary variables for estimating GRM with non-ignorable missing data
Zhang, Jiwei
Zhang, Zhaoyuan
Tao, Jian
COMPUTATIONAL STATISTICS, 2021, 36 (04) : 2643 - 2669
[9] Bayesian prediction of spatial data with non-ignorable missingness
Zahmatkesh, Samira
Mohammadzadeh, Mohsen
STATISTICAL PAPERS, 2021, 62 (05) : 2247 - 2268
[10] Bayesian joint modeling of binomial and rank response with non-ignorable missing data for primate cognition
Aghayerashti, Maryam
Samani, Ehsan Bahrami
Ganjali, Mojtaba
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, 2024, 53 (10) : 3758 - 3778

← 1 2 3 4 5 →