题名 | A novel MM algorithm and the mode-sharing method in Bayesian computation for the analysis of general incomplete categorical data |
作者 | |
通讯作者 | Liu, Yin |
发表日期 | 2019-12
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DOI | |
发表期刊 | |
ISSN | 0167-9473
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EISSN | 1872-7352
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卷号 | 140页码:122-143 |
摘要 | Incomplete categorical data often occur in the fields such as biomedicine, epidemiology, psychology, sports and so on. In this paper, we first introduce a novel minorization-maximization (MM) algorithm to calculate the maximum likelihood estimates (MLEs) of parameters and the posterior modes for the analysis of general incomplete categorical data. Although the data augmentation (DA) algorithm and Gibbs sampling as the corresponding stochastic counterparts of the expectation-maximization (EM) and ECM algorithms are developed very well, up to now, little work has been done on creating stochastic versions to the existing MM algorithms. This is the first paper to propose a mode-sharing method in Bayesian computation for general incomplete categorical data by developing a new acceptance-rejection (AR) algorithm aided with the proposed MM algorithm. The key idea is to construct a class of envelope densities indexed by a working parameter and to identify a specific envelope density which can overcome the four drawbacks associated with the traditional AR algorithm. The proposed mode-sharing based AR algorithm has three significant characteristics: (I) it can automatically establish a family of envelope densities {g(lambda)(.): lambda is an element of S-lambda} indexed by a working parameter lambda, where each member in the family shares mode with the posterior density; (II) with the onedimensional grid method searching over the finite interval S-lambda,S- it can identify an optimal working parameter lambda(opt) by maximizing the theoretical acceptance probability, yielding a best easy-sampling envelope density g lambda(opt) (.), which is more dispersive than the posterior density; (III) it can obtain the optimal envelope constant c(opt) by using the mode-sharing theorem (indicating that the high-dimensional optimization can be completely avoided) or by using the proposed MM algorithm again. Finally, a toy model and three real data sets are used to illustrate the proposed methodologies. (C) 2019 Published by Elsevier B.V. |
关键词 | |
相关链接 | [来源记录] |
收录类别 | |
语种 | 英语
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学校署名 | 第一
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资助项目 | Research Grant Council of the Hong Kong Special Administrative Region, China[UGC/FDS14/P01/14]
; Research Grant Council of the Hong Kong Special Administrative Region, China[UGC/FDS14/P01/16]
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WOS研究方向 | Computer Science
; Mathematics
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WOS类目 | Computer Science, Interdisciplinary Applications
; Statistics & Probability
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WOS记录号 | WOS:000478704800008
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出版者 | |
EI入藏号 | 20192707142735
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EI主题词 | Bayesian networks
; Clustering algorithms
; Computation theory
; Maximum likelihood estimation
; Optimization
; Stochastic systems
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EI分类号 | Computer Theory, Includes Formal Logic, Automata Theory, Switching Theory, Programming Theory:721.1
; Information Sources and Analysis:903.1
; Combinatorial Mathematics, Includes Graph Theory, Set Theory:921.4
; Optimization Techniques:921.5
; Statistical Methods:922
; Systems Science:961
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ESI学科分类 | MATHEMATICS
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来源库 | Web of Science
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引用统计 |
被引频次[WOS]:1
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成果类型 | 期刊论文 |
条目标识符 | http://kc.sustech.edu.cn/handle/2SGJ60CL/25063 |
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 518055, Guangdong, Peoples R China 2.Zhongnan Univ Econ & Law, Sch Math & Stat, Wuhan 430073, Hubei, Peoples R China 3.Hang Seng Univ Hong Kong, Sch Decis Sci, Dept Math & Stat, Siu Lek Yuen,Shatin, 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,Liu, Yin,Tang, Man-Lai,et al. A novel MM algorithm and the mode-sharing method in Bayesian computation for the analysis of general incomplete categorical data[J]. COMPUTATIONAL STATISTICS & DATA ANALYSIS,2019,140:122-143.
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APA |
Tian, Guo-Liang,Liu, Yin,Tang, Man-Lai,&Li, Tao.(2019).A novel MM algorithm and the mode-sharing method in Bayesian computation for the analysis of general incomplete categorical data.COMPUTATIONAL STATISTICS & DATA ANALYSIS,140,122-143.
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MLA |
Tian, Guo-Liang,et al."A novel MM algorithm and the mode-sharing method in Bayesian computation for the analysis of general incomplete categorical data".COMPUTATIONAL STATISTICS & DATA ANALYSIS 140(2019):122-143.
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条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | 操作 | |
Tian-2019-A novel MM(1749KB) | -- | -- | 限制开放 | -- |
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