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Supervised cluster analysis of non-Gaussian functional data
澳门新葡8455最新网站:2019年08月05日 09:27 点击数:

报告人:Jiakun Jiang

报告地点:图书馆一楼会议室

报告澳门新葡8455最新网站:2019年08月06日星期二15:00-16:00

邀请人:朱文圣

报告摘要:

In this paper we study cluster analysis of functional regression with a random response curve and vector covariates. We propose a mixed transformation functional regression model with an unknown number of clusters. Compared to the existing cluster analysis of functional regression, our model has several advantages. First, our model is free from normality assumption. Second, it is supervised, in that the clustering is based on the relationship between the functional response and covariates. Finally, we allow the number of clusters to be unknown a priori. We propose a combination of penalized likelihood and estimating equation methods to estimate the number of clusters, regression parameters and transformation function simultaneously. We establish theoretical properties, including Sqrt(n)-consistency and asymptotic normality, for the proposed estimators. Extensive simulation results show that the proposed estimation procedure works very well. The proposed method is utilized to analyze housing market conditions in China from 2007 to 2014, which leads to some interesting findings.

主讲人概况:

Jiakun Jiang earned his PHD from the Center of Statistical Research, Southwest University of Finance and Economics in 2018, supervised by Dr.Huazhen Lin. Now he is a post-doc at Tsinghua University, working with Dr.Lijian Yang. He is primarily interested in functional data analysis, threshold model, semiparametric model, high-dimension variable selection.

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