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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/106414


    Title: Spatial-Temporal Model for Count Data
    Authors: Chang, Ya-Mei
    Keywords: Poisson-lognormal model;Spatial-temporal process;Disease maps;Lasso;group Lasso
    Date: 2015/06/28
    Issue Date: 2016-04-27 11:12:18 (UTC+8)
    Abstract: 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 and a stationary process. 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 data sets.
    Relation: 第二十四屆南區統計研討會暨2015中華機率統計學會年會及學術研討會
    Appears in Collections:[統計學系暨研究所] 會議論文

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