Li, Shunyong; Qian, Yuhua; Zhang, Xiaoqin; Niu, Jianyong Two-stage estimation about heteroscedastic model based on variable selection and cluster analysis. (Chinese. English summary) Zbl 1424.62116 Chin. J. Appl. Probab. Stat. 34, No. 2, 191-200 (2018). Summary: 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 being incomplete. In this paper, we propose to select several variables for grouping using variable selection method, then use \(k\)-mean algorithm to clustering, 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. MSC: 62J05 Linear regression; mixed models 62J10 Analysis of variance and covariance (ANOVA) 62H30 Classification and discrimination; cluster analysis (statistical aspects) Keywords:heteroscedastic model; variable selection; \(k\)-mean algorithm; two-stage estimation PDFBibTeX XMLCite \textit{S. Li} et al., Chin. J. Appl. Probab. Stat. 34, No. 2, 191--200 (2018; Zbl 1424.62116) Full Text: DOI