Non-parametric method for filling in the missing value for cross-sectional dataset: A validation on the per capita GDP data at county level in China
文献类型：SCI/SSCI论文
| 作者 | Deng X. Z. ; Fang Y. ; Lin Y. Z. ; Yuan Y. W. |
| 发表日期 | 2012 |
| 关键词 | Non-parametric method stepwise multiple regression interpolation per capita GDP China interpolation |
| 英文摘要 | When dealing with the observation with missing values, we used to get them by means of mathematical interpolation. Compared with the traditional methods for parametric interpolation including linear interpolation, spline interpolation, kriging interpolation, etc., which sometimes export so paradoxical results that there are quite a lot of debates on the reliability of rationale and application, the non-parametric methods are becoming more and more popular to interpolate the missing values for the cross sectional dataset. In this paper, a non-parametric method is introduced and its feasibility of filling in missing values of per capita GDP data at county level for China is illustrated and verified. The results indicate that the non-parametric method produces essentially unbiased estimates by using kernel density function based on a sample drawn from all the observations. So it appears that the actual performance of non-parametric model can be quite helpful to fill in the missing values with a large sample of observation and the non-parametric extrapolation methods tested in this empirical study could be applied in other similar studies. |
| 出处 | Journal of Food Agriculture & Environment |
| 卷 | 10 |
| 期 | 3-4 |
| 页 | 1350-1354 |
| 收录类别 | SCI |
| 语种 | 英语 |
| ISSN号 | 1459-0255 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/30899]  |
| 专题 | 地理科学与资源研究所_历年回溯文献 |
| 推荐引用方式 GB/T 7714 | Deng X. Z.,Fang Y.,Lin Y. Z.,et al. Non-parametric method for filling in the missing value for cross-sectional dataset: A validation on the per capita GDP data at county level in China. 2012. |
入库方式： OAI收割
来源：地理科学与资源研究所
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