Multiple Imputation for Missing Data in Inventory on Dominant Tree Height

机译：优势树高度上库存数据缺失的多重估算

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In the paper we discuss the application of MI in the missing data analysis. Using MCMC method of Bayesian principle and the proc MI and proc Mianalyze of SAS software we make multiple imputations for missing data of forest resource investigation and get the result analysis in different losing-rate. Eventually we draw the conclusion that MI methods is a valid tool for missing data of forest resource investigation.

机译：在本文中，我们讨论了MI在缺失数据分析中的应用。利用贝叶斯原理的MCMC方法和SAS软件的proc MI和proc Mianalyze，对森林资源调查的缺失数据进行了多种归因，并以不同的损失率进行了结果分析。最终我们得出结论，MI方法是缺失森林资源调查数据的有效工具。

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来源
《2009 International Institute of Applied Statistics Studies(2009 国际应用统计学术研讨会）论文集》|2009年|1-6|共6页
会议地点 Qingdao(CN)
作者
ZHANG Xiangyun; SONG Hongfeng;
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作者单位

Zhejiang Forestry College, Lin'an;

Zhejiang Province, 311300;

Zhejiang Forestry College, Lin'an;

Zhejiang Province, 311300;

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会议组织
原文格式 PDF
正文语种 eng
中图分类统计学;
关键词
Missing Data; Parameter Estimation; Multiple Imputation; MCMC;

机译：缺失数据;参数估计;多次归集; MCMC;

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Multiple Imputation for Missing Data in Inventory on Dominant Tree Height

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