[1]何朝兵.缺失数据下Logistic回归多变点模型的贝叶斯估计[J].南京师范大学学报(自然科学版),2016,39(04):0.[doi:10.3969/j.issn.1001-4616.2016.04.004]
　He Chaobing.Bayesian Estimation of Logistic Regression Modelwith Multiple Change Points for Missing Data[J].Journal of Nanjing Normal University(Natural Science Edition),2016,39(04):0.[doi:10.3969/j.issn.1001-4616.2016.04.004]
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缺失数据下Logistic回归多变点模型的贝叶斯估计()
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《南京师范大学学报》（自然科学版）[ISSN:1001-4616/CN:32-1239/N]

卷:
第39卷
期数:
2016年04期
页码:
0
栏目:
·数学与计算机科学·
出版日期:
2016-12-30

文章信息/Info

Title:
Bayesian Estimation of Logistic Regression Modelwith Multiple Change Points for Missing Data
文章编号:
1001-4616(2016)04-0014-05
作者:
何朝兵
安阳师范学院数学与统计学院,河南 安阳 455000
Author(s):
He Chaobing
He Chaobing
关键词:
完全数据似然函数; 满条件分布; 筛选法; Gibbs抽样; Metropolis-Hastings算法
Keywords:
complete-data likelihood function; full conditional
分类号:
O212.4; O212.8
DOI:
10.3969/j.issn.1001-4616.2016.04.004
文献标志码:
A
摘要:
利用随机的方法填充了缺失数据,获得了Logistic回归多变点模型的完全数据似然函数. 研究了变点位置等未知参数的满条件分布. 利用筛选法和Metropolis-Hastings算法对参数进行抽样,把Gibbs样本的均值作为参数的贝叶斯估计. 随机模拟的结果表明估计的精度较高.
Abstract:
The missing data is filled in by a random way. The complete-data likelihood function of logistic regression model with multiple change points is obtained. The full conditional distributions of change-point positions and other unknown parameters are studied. All the parameters are sampled by screening method and Metropolis-Hastings algorithm,and the means of Gibbs samples are taken as Bayesian estimations of the parameters. Random simulation results show that the estimations are fairly accurate.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2015-09-20.
基金项目:河南省高等学校重点科研项目(16A110001).
通讯联系人:何朝兵,硕士,讲师,研究方向:概率统计. E-mail:chaobing5@163.com
更新日期/Last Update: 2016-12-31