南方科技大学知识苑(SUSTech KC): New expectation-maximization-type algorithms via stochastic representation for the analysis of truncated normal data with applications in biomedicine

题名	New expectation-maximization-type algorithms via stochastic representation for the analysis of truncated normal data with applications in biomedicine
作者	Tian, Guo-Liang1 ; Ju, Da 2; Yuen, Kam Chuen 2; Zhang, Chi 2
通讯作者	Zhang, Chi
发表日期	2018-08
DOI	10.1177/0962280216681598
发表期刊	STATISTICAL METHODS IN MEDICAL RESEARCH 影响因子和分区
ISSN	0962-2802
EISSN	1477-0334
卷号	27 期号:8页码:2459-2477
摘要	To analyze univariate truncated normal data, in this paper, we stochastically represent the normal random variable as a mixture of a truncated normal random variable and its complementary random variable. This stochastic representation is a new idea and it is the first time to appear in literature. According to this stochastic representation, we derive important distributional properties for the truncated normal distribution and develop two new expectation-maximization algorithms to calculate the maximum likelihood estimates of parameters of interest for Type I data (without and with covariates) and Type II/III data. Bootstrap confidence intervals of parameters for small sample sizes are provided. To evaluate the performance of the proposed methods for the truncated normal distribution, in simulation studies, we first focus on the comparison of estimation results between including the unobserved data counts and excluding the unobserved data counts, and we next investigate the impact of the number of unobserved data on the estimation results. The plasma ferritin concentration data collected by Australian Institute of Sport and the blood fat content data are used to illustrate the proposed methods and to compare the truncated normal distribution with the half normal, the folded normal, and the folded normal slash distributions based on Akaike information criterion and Bayesian information criterion.
关键词	AIC BIC bootstrap method complementary random variable EM algorithm stochastic representation truncated normal distribution
相关链接	[来源记录]
收录类别	SCI
语种	英语
学校署名	第一
WOS研究方向	Health Care Sciences & Services ; Mathematical & Computational Biology ; Medical Informatics ; Mathematics
WOS类目	Health Care Sciences & Services ; Mathematical & Computational Biology ; Medical Informatics ; Statistics & Probability
WOS记录号	WOS:000438616300016
出版者	SAGE PUBLICATIONS LTD
ESI学科分类	MATHEMATICS
来源库	Web of Science
引用统计	被引频次[WOS]：7
成果类型	期刊论文
条目标识符	http://sustech.caswiz.com/handle/2SGJ60CL/27432
专题	理学院_数学系工学院_材料科学与工程系
作者单位	1.Southern Univ Sci & Technol, Dept Math, Shenzhen, Peoples R China 2.Univ Hong Kong, Dept Stat & Actuarial Sci, Pokfulam Rd, Hong Kong, Hong Kong, Peoples R China
第一作者单位	数学系
第一作者的第一单位	数学系
推荐引用方式 GB/T 7714	Tian, Guo-Liang,Ju, Da,Yuen, Kam Chuen,et al. New expectation-maximization-type algorithms via stochastic representation for the analysis of truncated normal data with applications in biomedicine[J]. STATISTICAL METHODS IN MEDICAL RESEARCH,2018,27(8):2459-2477.
APA	Tian, Guo-Liang,Ju, Da,Yuen, Kam Chuen,&Zhang, Chi.(2018).New expectation-maximization-type algorithms via stochastic representation for the analysis of truncated normal data with applications in biomedicine.STATISTICAL METHODS IN MEDICAL RESEARCH,27(8),2459-2477.
MLA	Tian, Guo-Liang,et al."New expectation-maximization-type algorithms via stochastic representation for the analysis of truncated normal data with applications in biomedicine".STATISTICAL METHODS IN MEDICAL RESEARCH 27.8(2018):2459-2477.