学术报告
Model Selection and Model Averaging for Nonlinear Regression Models-Qingfeng Liu (Otaru University of Commerce, Japan)
题目：Model Selection and Model Averaging for Nonlinear Regression Models
报告人： Qingfeng Liu (Otaru University of Commerce, Japan)
摘要：This paper considers the problem of model selection and model averaging for regression models which can be nonlinear in their parameters and variables. We propose a new information criterion called nonlinear model information criterion (NIC), which is proved to be an asymptotically unbiased estimator of the risk function under nonlinear settings. We also develop a nonlinear model averaging method (NMA) and extend NIC to NICMA criterion, which is the corresponding weight choosing criterion for NMA. By taking account of the complexity of model forms into the penalty term, NIC and NICMA achieve significant gain of performance. The optimality of NMA, convergence of the selected weight and other theoretical properties are proved. Simulation results show that NIC and NMA lead to relatively lower risks compared with alternative model selection and model averaging methods under most situations.
时间：3月22日（周三）下午4:00-5:00
地点：首都师范大学本部新教二楼725教室
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