卜瓦松-對數常態模式於時空資料之研究

機構典藏 > College of Business and Management > Graduate Institute & Department of Statistics > Research Paper > Item 987654321/101285

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/101285

Title:	卜瓦松-對數常態模式於時空資料之研究
Other Titles:	Poisson-Lognormal Model for Spatial-Temporal Data
Authors:	張雅梅
Contributors:	淡江大學統計學系
Date:	2013-08
Issue Date:	2015-04-21 14:11:29 (UTC+8)
Abstract:	在流行病學研究上，利用計數資料來估計整個研究區域的疾病地圖是非常重要的議題。我們在卜瓦松-對數常態模式的架構下，發展一時空模式，將經對數轉換後的條件期望病例人數表示為數個基底函數的線性組合，將平均數與變異數估計問題視為迴歸分析，利用傳統的Lasso法與group Lasso法來挑選適合的基底函數及估計平均數與共變異數。此種方法能描繪平穩或非平穩過程，且在處理龐大的空間資料上，能迅速有效地被運算。 In epidemiology, disease mapping using count data is a very important issue. Under a Poisson-lognormal model, we develop a spatial-temporal process. The log transformation of the conditional expected number of cases is decomposed as a linear combination of basis functions. The problem of mean and covariance estimations can be considered as a regression. A subset selection method of Lasso and group Lasso are used to choose a suitable subset of the basis functions and estimate the mean and covariances. This method can characterize either non-stationary or nearly stationary spatial processes, and is computationally efficient for large spatial data sets.
Appears in Collections:	[Graduate Institute & Department of Statistics] Research Paper

Files in This Item:

There are no files associated with this item.

Loading...