| 正态模型缺失数据的贝叶斯和jackknife多重插补法的比较 |
投稿时间：2019-12-05 修订日期：2019-12-12  点此下载全文 |
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| 基金项目:国家自然科学基金项目“基于小波框架的散乱数据重构及其在计算生物中的应用”（11771120） |
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| 中文摘要:数据缺失是统计调查中经常存在的问题，若是少量缺失则可以利用删除法；若缺失值较多，利用删除法则会丢失大量有用信息，这时候就需利用插补法来补全数据，从而减少对统计分析的影响。本文通过运用贝叶斯多重插补法和jackknife多重插补法对粮食产量、播种面积、有效灌溉面积和化肥施用量的数据进行模拟分析，来比较出这两种方法对农业调查中缺失数据的插补效果。研究发现，利用这两种方法构建的模型都有较好的估计结果，但是贝叶斯多重插补法更为精确，而jackknife法在操作方面则更为简单。 |
| 中文关键词:贝叶斯多重插补法 jackknife多重插补法 缺失数据 |
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| Comparison of Bayesian and Jackknife Multiple Interpolation Methods with Missing Data in Normal Model |
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| Abstract:Missing data is a common problem in statistical surveys. If there are a few missing, you can use the deletion method. If there are many missing values, the deletion method will lose a lot of useful information. In this case, you need to use the interpolation method to complete the data. Thereby reducing the impact on statistical analysis. This paper simulates the data of grain yield, planting area, effective irrigated area and chemical fertilizer application by using bayesian multiple imputation method and jackknife multiple imputation method to compare these two methods in agricultural survey. The study found that the models constructed by these two methods have good estimation results, but the bayesian multiple interpolation method is more accurate, and the jackknife method is simpler in operation. |
| keywords:bayesian multiple interpolation method jackknife multiple interpolation method missing data |
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