报告人：Lin Jinguan (林金官教授，南京审计大学)

报告题目：A Robust and Efficient Estimation Method For Nonparametric Models with Mixed Discrete and Continuous Data

报告摘要：Nonparametric models with with mixed discrete and continuous regressors have applications to many fields, such as medicine, economics and finance. However, most existing methods based on least squares or likelihood are sensitive due to the preference of outliers or the error distribution is heavy tailed. In this paper, a new robust and efficient estimation procedure based on local modal regression is proposed for nonparametric models with mixed discrete and continuous regressors, and a modified EM algorithm is introduced to estimate the proposed estimator. Under some regular conditions, the large sample theory is established for the proposed estimators. We show that the proposed estimators are as asymptotically efficient as the least-square based estimators when there are no outliers and the error distribution is normal. The simulations and real data analysis are conducted to illustrate the finite sample performance of the proposed method.

报告时间：2017年12月15日（星期五）下午2：00 - 2：45.

报告地点：科技楼南楼715