主講人

陳春樹 教授 (國立中央大學 統計研究所)

講題

Model selection for multiple spatial correlation data

時間

星期四 下午2:00~4:00

地點

數學系1樓21113演講室

摘要

In spatial regression analysis, a suitable specification of the mean regression model is crucial for unbiased analysis. Suitably account for the underlying spatial correlation structure of the response variables is also an important issue. Here, we focus on selection of an appropriate mean model in spatial regression analysis under a general anisotropic nested spatial correlation structure. We propose a distribution-free model selection criterion which is an estimate of the weighted mean squared error based on assumptions only for the first two moments of the responses. Simulations under the settings of covariate selection reveal that the proposed criterion performs well regardless of the underlying spatial correlation structure is nested, non-nested, isotropic, or anisotropic. Finally, a real data example regarding the fine particulate matter concentration is analyzed for illustration.