学术报告

Least Squares Estimation for Nonlinear Regression Models with Heteroscedasticity

阅读次数:967

题目:Least Squares Estimation for Nonlinear Regression Models with Heteroscedasticity
报告人:Prof. Wang Qiying(悉尼大学数学与统计学院)
时间:2018年12月10日(周一)下午16:00开始
地点:致远楼101室
摘要:This paper develops an asymptotic theory for general nonlinear regression models, establishing a new framework on least squares estimation that is easy to apply for various nonlinear regression models with heteroscedasticity. This paper explores an application of the framework to nonlinear regression models with nonstationarity and heteroscedasticity. Accompanying with these main results, this paper provides a maximum inequality for a class of martingales and establishes some new results on convergence to a local time and convergence to a mixture of normal distributions, which are interested in their own rights.

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