GENERALIZED MAXIMUM ENTROPY ESTIMATION OF A FIRST ORDER SPATIAL AUTOREGRESSIVE MODEL

2004 ◽  
pp. 199-234 ◽  
Author(s):  
Thomas L. Marsh ◽  
Ron C. Mittelhammer
Keyword(s):  
Maximum Entropy ◽  
Autoregressive Model ◽  
Spatial Autoregressive Model ◽  
First Order ◽  
Entropy Estimation ◽  
Generalized Maximum Entropy ◽  
Spatial Autoregressive ◽  
Maximum Entropy Estimation

A Note on Small Sample Properties of Estimators in a First-Order Spatial Autoregressive Model

1982 ◽  
Vol 14 (8) ◽  
pp. 1023-1030 ◽  
Author(s):  
L Anselin
Keyword(s):  
Monte Carlo Simulation ◽  
Autoregressive Model ◽  
Ad Hoc ◽  
Simulation Experiment ◽  
Small Sample ◽  
Bayesian Estimator ◽  
Spatial Autoregressive Model ◽  
First Order ◽  
Small Sample Properties ◽  
Spatial Autoregressive

This note considers a Bayesian estimator and an ad hoc procedure for the parameters of a first-order spatial autoregressive model. The approaches are derived, and their small sample properties compared by means of a Monte Carlo simulation experiment.

scholarly journals Information Theory Estimators for the First-Order Spatial Autoregressive Model

Entropy ◽  
2012 ◽  
Vol 14 (7) ◽  
pp. 1165-1185 ◽  
Author(s):  
Evgeniy V. Perevodchikov ◽  
Thomas L. Marsh ◽  
Ron C. Mittelhammer
Keyword(s):  
Information Theory ◽  
Autoregressive Model ◽  
Spatial Autoregressive Model ◽  
First Order ◽  
Spatial Autoregressive

scholarly journals Generalized Maximum Entropy Estimation of Linear Models

2017 ◽  
Vol 17 (1) ◽  
pp. 240-249 ◽  
Author(s):  
Paul Corral ◽  
Daniel Kuehn ◽  
Ermengarde Jabir
Keyword(s):  
Maximum Likelihood ◽  
Maximum Entropy ◽  
Linear Models ◽  
Estimation Method ◽  
Information Theoretic ◽  
Entropy Estimation ◽  
Generalized Maximum Entropy ◽  
Maximum Entropy Estimation ◽  
Distributional Assumptions ◽  
Highly Correlated

In this article, we describe the user-written gmentropylinear command, which implements the generalized maximum entropy estimation method for linear models. This is an information-theoretic procedure preferable to its maximum likelihood counterparts in many applications; it avoids making distributional assumptions, works well when the sample is small or covariates are highly correlated, and is more efficient than its maximum likelihood equivalent. We give a brief introduction to the generalized maximum entropy procedure, present the gmentropylinear command, and give an example using the command.

scholarly journals Comparison of entropy measures in generalized maximum entropy estimation

2018 ◽  
Vol 1053 ◽  
pp. 012021
Author(s):  
Wilawan Srichaikul ◽  
Woraphon Yamaka ◽  
Paravee Maneejuk ◽  
Songsak Sriboonchitta
Keyword(s):  
Maximum Entropy ◽  
Entropy Estimation ◽  
Generalized Maximum Entropy ◽  
Entropy Measures ◽  
Maximum Entropy Estimation
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