Inference for Spatial Trends
Non-stationary spatial models are widely applicable in diverse disciplines, ranging from bio-medical sciences to geophysical studies. In many of theses applications, testing for structural changes in the trend and testing the specific form of the trend are highly relevant. A novel statistics based on a discrepancy measure over small regions is proposed in this paper. Such a measure can be used to construct tests for structural trends and to identify change boundaries of the trends. By virtue of the m-dependence approximation of a stationary random eld, asymptotic properties and limit distributions of these tests are established. The method is illustrated by simulations and data analysis.
Overview
Abstract
Non-stationary spatial models are widely applicable in diverse disciplines, ranging from bio-medical sciences to geophysical studies. In many of theses applications, testing for structural changes in the trend and testing the specific form of the trend are highly relevant. A novel statistics based on a discrepancy measure over small regions is proposed in this paper. Such a measure can be used to construct tests for structural trends and to identify change boundaries of the trends. By virtue of the m-dependence approximation of a stationary random eld, asymptotic properties and limit distributions of these tests are established. The method is illustrated by simulations and data analysis.
Brief Biography
Professor Ngai Hang Chan is currently the holder of Choh-Ming Li Chair Professor of Statistics at The Chinese University of Hong Kong. An elected fellow of the Institute of Mathematical Statistics and the American Statistical Association, he is also the holder of the Chang Jiang Scholar Visiting Chair Professor of Statistics of the Renmin University of China and the recipient of the 100-Men Scholars as a Visiting Professor at South-western University of Finance and Economics. Before returning to Hong Kong, Professor Chan was a Professor of Statistics at the Carnegie Mellon University in Pittsburgh, USA. Professor Chan is a world renowned scholar in time series, econometrics, risk management and statistical finance. He is an editorial board member for a number of influential scientific journals, including but not limited to, JASA, JBES, Econometric Theory, Bernoulli and Statistica Sinica, just to name a few.
Refreshments:
Light refreshments will be served around 15:45.
