文献详情
Likelihood-based quantile mixed effects models for longitudinal data with multiple features via MCEM algorithm
文献类型：期刊
通讯作者：Tian, YZ (reprint author), Henan Univ Sci & Technol, Sch Math & Stat, Luoyang 471023, Peoples R China.
期刊名称：COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION影响因子和分区
年：2020
卷：49
期：2
页码：317-334
ISSN：0361-0918
关键词：Censoring; covariates in error; heavy-tailed distribution; joint modeling; MCEM algorithm; mixed effects models; quantile regression analysis
所属部门：统计学院
摘要：In longitudinal data analysis, many data features are frequently encountered in practice. For example, variates are usually measured with censoring, substantial errors and non-normal feature, etc. In such case, it is impossible to give a precise result by conducting statistical inference of separate analysis. The joint modeling may be a considerable alternative. The superiority of joint modeling is that it can implement a simultaneous analysis for longitudinal mixed models with random effects, c ...More
In longitudinal data analysis, many data features are frequently encountered in practice. For example, variates are usually measured with censoring, substantial errors and non-normal feature, etc. In such case, it is impossible to give a precise result by conducting statistical inference of separate analysis. The joint modeling may be a considerable alternative. The superiority of joint modeling is that it can implement a simultaneous analysis for longitudinal mixed models with random effects, censoring, nor-normal errors, errors in covariates and heavy-tailed feature by pooling all data information together. However, most of traditional modeling methods aim at depicting the average variation of outcome variable conditionally on covariates, which may result in non-robust estimation results when suffering outliers or non-normal errors. Quantile regression provides an attractive alternative to model longitudinal data with multiple data features, which can draw a complete picture of the conditional distributions of outcome variable and bring out robust estimation results. In this paper, we conduct the likelihood-based joint quantile regression for longitudinal mixed models by accounting for the above multiple data features simultaneously. Based on the asymmetric Laplace distribution (ALD), the Monte Carlo Expectation-Maximization (MCEM) algorithm is employed to address the estimation problem. Finally, some Monte Carlo simulations are conducted and an AIDS data analysis is provided to illustrate the developed procedures. ...Hide

DOI：10.1080/03610918.2018.1484477
百度学术：Likelihood-based quantile mixed effects models for longitudinal data with multiple features via MCEM algorithm
语言：外文
被引频次：1
基金：National Natural Science Foundation of ChinaNational Natural Science Foundation of China [11501167]; China Postdoctoral Science FoundationChina Postdoctoral Science Foundation [2017M610156]; Young academic leaders project of Henan University of Science and Technology [13490008]; Major research projects of philosophy and social science of the Chinese Ministry of Education [15JZD015]; Renmin University of China [15XNL008]; Research Grant Council of the Hong Kong Special Administration RegionHong Kong Research Grants Council [UGC/FDS14/P01/16]
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