.png){kind=icon}
返回
A matrix exponential spatial specification
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jeconom.2006.09.007.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We introduce the matrix exponential as a way of modelling spatially dependent data. The matrix exponential spatial specification (MESS) simplifies the log-likelihood allowing a closed form solution to the problem of maximum-likelihood estimation, and greatly simplifies the Bayesian estimation of the model. The MESS can produce estimates and inferences similar to those from conventional spatial autoregressive models, but has analytical, computational, and interpretive advantages. We present maximum likelihood and Bayesian approaches to the estimation of this spatial model specification along with methods of model comparisons over different explanatory variables and spatial specifications. (C) 2006 Elsevier B.V. All rights reserved.
Keyword:
spatial autoregression
bayesian
maximum likelihood
log-determinants
matrix exponentials
model comparison
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4
论文数:
5.2K
被引数:
3.0W
机构
暂无机构信息
引用论文
.png){kind=icon}
分享收藏
查看更多内容