Spatial-Temporal Model for Count Data

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Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/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:	[Graduate Institute & Department of Statistics] Proceeding

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