Nonlinear Regression with Nonstationarity and Heteroscedasticity
报告人： Qiying Wang, University of Sydney
地点：Room 217, Guanghua Building No. 2
This paper develops an asymptotic theory of nonlinear least squares estimation by establishing a new framework that can be easily applied to various nonlinear regression models with heteroscedasticity. This paper explores an application of the framework to nonlinear regression models with nonstationarity and heteroscedasticity. In addition to 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.
About the Speaker:
Qiying Wang is a Professor of Statistics and Econometrics at the University of Sydney, Australia, with well-established expertise in classical limit theory and asymptotics. He works on nonstationary time series econometrics, nonparametric statistics, econometric theory, martingale limit theory and self-normalized limit theory. He was an Australian Research Fellow from 2007 to 2012 and his research has been constantly supported by Australian Research Council since 2004. He has published over 80 research papers and over half of them appeared in the top ranked Probability, Statistics and Econometrics journals such as Econometrica, Annals of Probability, Annals of Statistics, Journal of Econometrics and Econometric Theory. He is one of the three recipients of 2017 Econometric Theory Plura Scripsit Award. His monograph entitled "Limit theorems for nonlinear cointegrating regression" systematically introduces the machinery of theoretical development in nonlinear cointegrating regression.
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