我国各地区教育发展水平的无交叉分位回归模型
Noncrossing Quantile Regression Modelling for Regional Education Development Data in China
DOI: 10.12677/SA.2014.32006, PDF, HTML, XML, 下载: 2,557  浏览: 9,232  国家自然科学基金支持
作者: 杨亚琦, 田茂再:中国人民大学应用统计科学研究中心,北京
关键词: 无交叉分位回归教育发展水平分位差异Noncrossing Quantile Regression Education Development Quatile Differentiation
摘要: 本文基于无交叉分位回归方法对我国各地区教育水平发展现状进行分析,研究了不同地区人口的平均受教育年限与人均GDP、教育经费和教师资源之间的关系。由于我国各地区教育水平发展不均衡,分位回归方法能够反映出数据的全貌,而无交叉分位回归能够使参数估计更加合理。研究结果表明,在教育水平较低的地区,教育发展受到经济条件的制约,在教育水平居中的地区,教育发展更多地受到教师资源的制约,而教育经费对不同地区的影响较为复杂。
Abstract: In this paper, we study regional education development in China based on noncrossing quantile regression and focus on the relationship between average years of education and average GDP, education budget and teacher resources. On account of the imbalance in regional education development, quantile regression offers a complete picture of data; on the other hand, noncrossing quantile regression makes the estimators more reasonable. The results prove that economic condition hinders the development of education in low level region, while in middle level region teacher resources play a more important role, and the effects of education fund on different regions are more complex.
文章引用:杨亚琦, 田茂再. 我国各地区教育发展水平的无交叉分位回归模型 [J]. 统计学与应用, 2014, 3(2): 37-43. http://dx.doi.org/10.12677/SA.2014.32006

参考文献

[1] 黄家泉 (2002) 教育区域化发展研究:地区经济发展不平衡对教育的影响. 山西人民出版社, 太原.
[2] 刘见芳 (2004) 我国高等教育发展水平地区差异研究. 硕士论文, 清华大学, 北京.
[3] 汪明 (2005) 义务教育均衡发展与若干保障机制——部分地区的政策及实践分析. 教育发展研究, 10, 40-44.
[4] 刘红梅 (2013) 中国各地区教育发展水平差异的实证研究. 数理统计与管理, 4, 586-594.
[5] 张海英 (2013) 我国区域高等教育水平的综合评价. 统计与决策, 1, 66-67.
[6] Koenker, R. (1984) A note on L-estimators for linear models. Statistics and Probability Letters, 2, 323-325.
[7] He, X. (1997) Quantile curves without crossing. The American Statistician, 51, 186-192.
[8] Wu, Y. and Liu, Y. (2009) Stepwise multiple quantile regression estimation using non-crossing con-straints. Statistics and Its Interface, 2, 299-310.
[9] Hall, P., Wolff, R.C.L. and Yao, Q. (1999) Methods for estimating a conditional distribution function. Journal of the American Statistical Association, 94, 154-163.
[10] Dette, H. and Volgushev, S. (2008) Non-crossing non-parametric estimates of quantile curves. Journal of the Royal Statistical Society, 70, 609-627.
[11] Bondell, H.D., Reich, B.J. and Wang, H. (2010) Non-crossing quantile regression curve estimation. Biometrika, 97, 825-838.
[12] Koenker, R. and Hallock, K. (2001) Quantile regression. Journal of Economic Perspective, 15, 143-156.