Poisson spatial interaction model and maximum likelihood
Reducing the bias of the maximum likelihood estimator for the Poisson regression model
... Abstract We derive expressions for the first-order bias of the MLE for a Poisson regression model and show how these can be used to adjust the estimator and reduce bias without increasing MSE. The analytic ...
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On the Existence of the Maximum Likelihood Estimates for Poisson Regression
... Besides this robustness property, the estimator also has the advantage of being very well behaved. Indeed, it is easy to see that the Hessian is negative definite for all x and β, which facilitates the estimation and ...
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Maximum Likelihood Estimation for Spatial GLM Models
... discrete spatial responses. In these models, spatial correlation of the data is usually modelled by spatial latent ...the spatial latent variables which is more flexible distribution and also ...
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Further Simulation Evidence on the Performance of the Poisson Pseudo-Maximum Likelihood Estimator
... This issue has been addressed by Martínez-Zarzoso, Nowak-Lehmann and Vollmer (2007) and by Martin and Pham (2008). However, the simulations performed by these au- thors are flawed in that the data is not generated by a ...
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Asymptotic Comparison of Method of Moments Estimators and Maximum Likelihood Estimators of Parameters in Zero Inflated Poisson Model
... ZIP model is introduced in this section in the con- text of a practical ...situation. Maximum likelihood estima- tion of the parameters involved in the model is discussed in Section ...ZIP ...
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Modeling Spatial Spillovers of Divorce in Senegal Using Spatial Durbin Model: A Maximum Likelihood Estimation Approach
... Abstract: Spatial Durbin Model (SDM) is one of the family of spatial autoregressive ...SDM model to determine the spatial spillovers of divorce in ...The model parameters are ...
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Open population maximum likelihood spatial capture recapture
... with spatial capture- recapture, allowing for estimation of the effective area sampled and population ...population spatial capture-recapture is formulated as a hidden Markov model, allowing ...
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A ridge restricted maximum likelihood approach to spatial models
... port model can help explain the movement and reactions of the element once released into the atmosphere, estimates of the original sources (or sinks) contributing to this distribution can be ...inverse ...
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Open population maximum likelihood spatial capture-recapture
... population spatial capture-recapture (SCR) as a hidden Markov model (HMM) brings several ...HMM likelihood makes the open population SCR like- lihood, marginalised over all activity centres, ...
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Maximum Likelihood Estimator for Spatial Stochastic Frontier Models
... We applied both SARSF and SARARSF specifications of the spatial stochastic frontier model to a data set of European airports. Significant demand for airports benchmarking attracted academic researchers to ...
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Sieve maximum likelihood estimation for doubly semiparametric zero-inflated Poisson models
... zero-inflated Poisson (ZIP) regression model proposed by Mullahy [ 14 ] and de- veloped by Lambert [ 9 ] is among the widely used, but earlier work focused on the parametric ZIP ...ZIP model by ...
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A spatial autoregressive Poisson gravity model
... called spatial interaction models — represent a class of models that utilize origin-destination flow data to explain mean frequen- cies of interactions across ...
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A spatial autoregressive Poisson gravity model
... called spatial interaction models — represent a class of models that utilize origin-destination flow data to explain mean frequen- cies of interactions across ...
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Quasi maximum likelihood estimation for simultaneous spatial autoregressive models
... simultaneous spatial autoregres- sive model ...quasi maximum likelihood method to es- timate the ...the maximum likelihood estimator including consistency and limiting ...
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Unconditional maximum likelihood estimation of dynamic models for spatial panels
... unconditional likelihood function of the model formulated in ...exact likelihood function has shown to exist when applying this procedure to a standard linear regression model without ...
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Quasi maximum likelihood estimation for simultaneous spatial autoregressive models
... Introduction Spatial econometric models provide an effective way to study the spatial interactions among units and are widely used in urban, real estate, regional, public, agricultural, environmental ...the ...
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Consistency and asymptotic normality of the maximum likelihood estimator in a zero-inflated generalized Poisson regression
... Since Theorem 1 covers such important regression models as ZIP and GP regressions, we were also interested in investigating the accuracy of the normal approximation in these special models. In the case of a ZIP ...
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The Poisson quasi maximum likelihood estimator: A solution to the “adding up” problem in gravity models
... Indeed, Poisson is the only quasi-maximum likelihood estimator that preserves total trade ...preferring Poisson as a workhorse gravity model ...
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Maximum Likelihood Estimation of the Multivariate Normal Mixture Model
... 1 Introduction In finite mixture models it is assumed that data are obtained from a finite collection of populations and that the data within each population follow a standard distribution, typically normal, ...
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Maximum Likelihood Estimation of the Multivariate Normal Mixture Model
... 1 Introduction In finite mixture models it is assumed that data are obtained from a finite collection of populations and that the data within each population follow a standard distribution, typically normal, ...
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