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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/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:[統計學系暨研究所] 研究報告

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