报告题目：large dimensional factor anlaysis without moment constraint
报 告 人：孔新兵 南京审计大学教授
报告时间：2019年9月22日上午10：00-11：00
报告地点：数学楼 629
报告摘要：
Large-dimensional factor model has drawn much attention in the big-data era, in order to reduce the dimensionality and extract underlying features using a few latent common factors. Conventional methods for estimating the factor model typically requires finite fourth moment of the data, which ignores the effect of heavy-tailedness and thus may result in unrobust or even inconsistent estimation of the factor space and common components. In this paper, we propose to recover the factor space by performing principal component analysis to the spatial Kendall's tau matrix instead of the sample covariance matrix. In a second step, we estimate the factor scores by the ordinary least square (OLS) regression. Theoretically, we show that under the elliptical distribution framework the factor loadings and scores as well as the common components can be estimated consistently without any moment constraint. The convergence rates of the estimated factor loadings, scores and common components are provided. The finite sample performance of the proposed procedure is assessed through thorough simulations. An analysis of a macroeconomic dataset finds new factors in contrasting with existing results using PCA.
报告人简介：
孔新兵，现为南京审计大学教授，在统计学顶级期刊发表论文13篇，其中独立作者3篇。主持国家自然科学基金3项目。入选江苏省双创计划，江苏省青蓝工程中青年学术带头人。
