[[abstract]]This paper proposes a working estimating equation which is computationally easy to use for spatial count data. The proposed estimating equation is a modification of quasi-likelihood estimating equations without the need of correctly specifying the covariance matrix. Under some regularity conditions, we show that the proposed estimator has consistency and asymptotic normality. A simulation comparison also indicates that the proposed method has competitive performance in dealing with over-dispersion data from a parameter-driven model
In this paper we consider regression models for count data allowing for overdispersion in a Bayesian...
Spatial data analysis (SDA) has become an essential part of the researcher\u27s toolbox in regional ...
This paper proposes a new spatial lag regression model which addresses global spatial autocorrelatio...
[[abstract]]This paper proposes a working estimating equation which is computationally easy to use f...
AbstractThis paper consolidates the zero-inflated Poisson model for count data with excess zeros pro...
We consolidate the zero-inflated Poisson model for count data with excess zeros (Lambert, 1992) and ...
The paper suggests and studies count data models corresponding to previously studied spatial econome...
[[abstract]]We use the quasilikelihood concept to propose an estimating equation for spatial data wi...
We provide a computationally and statistically efficient method for estimating the parameters of a s...
Given a set of spatial data, often the desire is to estimate its covariance structure. For prac-tica...
Several spatial econometric approaches are available to model spatially correlated disturbances in c...
For spatial linear models, the classical maximum-likelihood estimators of both regression coefficien...
We display pseudo-likelihood as a special case of a general estimation technique based on proper sco...
In this paper we consider regression models for count data allowing for overdispersion in a Bayesian...
In this paper we consider regression models for count data allowing for overdispersion in a Bayesian...
In this paper we consider regression models for count data allowing for overdispersion in a Bayesian...
Spatial data analysis (SDA) has become an essential part of the researcher\u27s toolbox in regional ...
This paper proposes a new spatial lag regression model which addresses global spatial autocorrelatio...
[[abstract]]This paper proposes a working estimating equation which is computationally easy to use f...
AbstractThis paper consolidates the zero-inflated Poisson model for count data with excess zeros pro...
We consolidate the zero-inflated Poisson model for count data with excess zeros (Lambert, 1992) and ...
The paper suggests and studies count data models corresponding to previously studied spatial econome...
[[abstract]]We use the quasilikelihood concept to propose an estimating equation for spatial data wi...
We provide a computationally and statistically efficient method for estimating the parameters of a s...
Given a set of spatial data, often the desire is to estimate its covariance structure. For prac-tica...
Several spatial econometric approaches are available to model spatially correlated disturbances in c...
For spatial linear models, the classical maximum-likelihood estimators of both regression coefficien...
We display pseudo-likelihood as a special case of a general estimation technique based on proper sco...
In this paper we consider regression models for count data allowing for overdispersion in a Bayesian...
In this paper we consider regression models for count data allowing for overdispersion in a Bayesian...
In this paper we consider regression models for count data allowing for overdispersion in a Bayesian...
Spatial data analysis (SDA) has become an essential part of the researcher\u27s toolbox in regional ...
This paper proposes a new spatial lag regression model which addresses global spatial autocorrelatio...