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| 引用本文： | ��˳��,Ǯ�,������,ţ����. ���ڱ���ѡ��;�������������췽��ģ����[J]. 应用概率统计, 2018, 34(2): 191-200. DOI: 10.3969/j.issn.1001-4268.2018.02.007 |
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| 作者姓名： | ��˳�� Ǯ� ������ ţ���� |
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| 作者单位： | ??????????????, ??, 030006, ??????????????????????, ??, 030006, ????????????????????????????????????????, ??, 030006 |
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| 摘 要： | ![]()
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| 关 键 词： | ??????? ??????? k??? ????ι??? |
Two-Stage Estimation about Heteroscedastic Model Based on Variable Selection and Cluster Analysis |
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| LI ShunYong,QIAN YuHua,ZHANG XiaoQin,NIU JianYong. Two-Stage Estimation about Heteroscedastic Model Based on Variable Selection and Cluster Analysis[J]. Chinese Journal of Applied Probability and Statisties, 2018, 34(2): 191-200. DOI: 10.3969/j.issn.1001-4268.2018.02.007 |
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| Authors: | LI ShunYong QIAN YuHua ZHANG XiaoQin NIU JianYong |
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| Affiliation: | School of Mathematical Sciences, Shanxi University, Taiyuan, 030006, China, School of Computer and Information Technology,Shanxi University, Taiyuan, 030006, China, Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan, 030006, China |
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| Abstract: | ![]()
The heteroscedasticity is inevitable for the panel data modeling in economics. The two-stage estimation method is a better means to study the heteroscedasticity, in which the basis is to select only one independent variable for samples grouping, it can cause the information used is incomplete. In this paper, we propose to select several variables for grouping using variable selection method, then k-mean algorithm is used to cluster, so the samples classification can be achieved and the heteroscedasticity estimation can be obtained. The results of real example analysis show that the method presented in this paper has obvious advantages in effectiveness and feasibility. |
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| Keywords: | heteroscedastic model variable selection k-mean algorithm two-stage estimation |
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