GME Estimation of Spatial Structural Equations Models
نویسندگان
چکیده
منابع مشابه
GME Estimation of Spatial Structural Equations Models
The objective of this paper is to develop a GME formulation for the class of spatial structural equations models (S-SEM) into a panel data framework. In this respect, two innovatory aspects are introduced: (i) the formalization of the GME estimation approach of SEM to allow for spatial heterogeneity and spatial dependence (spatially sampled data); (ii) the extension of the methodology panel data.
متن کاملGme Estimation with Non-linearities and Spatial Dependence in Club Convergence Analysis
This paper assesses the existence of club convergence across countries by developing a two stage strategy, which employs information on clustering schemes identified by a mapping analysis and estimates a multiple-club spatial convergence model with non linearities and spatial dependence. Because of identification and collinearity problems, we introduce an entropy-based estimation procedure whic...
متن کاملA comparison of algorithms for maximum likelihood estimation of Spatial GLM models
In spatial generalized linear mixed models, spatial correlation is assumed by adding normal latent variables to the model. In these models because of the non-Gaussian spatial response and the presence of latent variables the likelihood function cannot usually be given in a closed form, thus the maximum likelihood approach is very challenging. The main purpose of this paper is to introduce two n...
متن کاملGeneralized spatial structural equation models.
It is common in public health research to have high-dimensional, multivariate, spatially referenced data representing summaries of geographic regions. Often, it is desirable to examine relationships among these variables both within and across regions. An existing modeling technique called spatial factor analysis has been used and assumes that a common spatial factor underlies all the variables...
متن کاملJoint Structural Estimation of Multiple Graphical Models
Gaussian graphical models capture dependence relationships between random variables through the pattern of nonzero elements in the corresponding inverse covariance matrices. To date, there has been a large body of literature on both computational methods and analytical results on the estimation of a single graphical model. However, in many application domains, one has to estimate several relate...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Classification
سال: 2011
ISSN: 0176-4268,1432-1343
DOI: 10.1007/s00357-011-9073-0