Regularized estimation for highly multivariate log Gaussian Cox processes

被引：0
|
作者：
Achmad Choiruddin
Francisco Cuevas-Pacheco
Jean-François Coeurjolly
Rasmus Waagepetersen
机构：
[1] Institut Teknologi Sepuluh Nopember (ITS),Department of Statistics
[2] Aalborg University,Department of Mathematical Sciences
[3] Université du Québec à Montréal (UQAM),Department of Mathematics
来源：
Statistics and Computing | 2020年 / 30卷
关键词：
Cross-pair correlation; Elastic net; LASSO; Log Gaussian Cox process; Multivariate point process; Proximal Newton method;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Statistical inference for highly multivariate point pattern data is challenging due to complex models with large numbers of parameters. In this paper, we develop numerically stable and efficient parameter estimation and model selection algorithms for a class of multivariate log Gaussian Cox processes. The methodology is applied to a highly multivariate point pattern data set from tropical rain forest ecology.
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页码：649 / 662
页数：13
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