吉布斯抽样在数据缺失中的应用及其R实现
Application of Gibbs Sampling in Data Missing and Execution in R
摘要: 数据缺失是统计研究中经常遇到的问题,文章在总结常见缺失数据的处理方法的基础上,提出了用Gibbs抽样方法来解决数据缺失问题,并通过R语言来实现这一过程,从而为数据缺失提供一种新的解决思路。实验结果表明,Gibbs抽样是一种效果比较理想的处理缺失数据的方法。
Abstract:
Data missing is a common problem in statistical research. Based on summarizing the common so-lutions, this paper proposes to solve the problem by Gibbs sampling, and achieve this process through the R language, so as to provide a new method. The experimental results show that Gibbs sampling is an ideal method to deal with missing data.
参考文献
[1]
|
曾莉, 辛涛, 张淑梅. 2PL模型的两种马尔可夫蒙特卡洛缺失数据处理方法比较[J]. 心理学报, 2009, 41(3): 276- 282.
|
[2]
|
张香云. Gibbs抽样在不同缺失率下的参数估计[J]. 统计与决策, 2008(4): 23-24.
|
[3]
|
陈晓林. 基于Gibbs抽样和EM算法的生物保守序列motif识别[D]: [硕士学位论文]. 苏州: 苏州大学, 2007.
|
[4]
|
Geman, S. and Geman, D. (1984) Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-6, 721-724.
https://doi.org/10.1109/TPAMI.1984.4767596
|
[5]
|
Hastings, W.K. (1970) Monte Carlo Sampling Methods Using Markov Chains and Their Applications. Biometrika, 57, 97-109. https://doi.org/10.1093/biomet/57.1.97
|
[6]
|
候雅文, 王斌会. 统计实验及R语言模拟[M]. 北京: 北京大学出版社, 2015.
|