Thomas L. Marsh

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Maximum entropy methods of parameter estimation are appealing because they impose no additional structure on the data, other than that explicitly assumed by the analyst. In this paper we prove that the data constrained GME estimator of the general linear model is consistent and asymptotically normal. The approach we take in establishing the asymptotic(More)
This study evaluates the economic consequences of hypothetical foot-and-mouth disease releases from the future National Bio and Agro Defense Facility in Manhattan, Kansas. Using an economic framework that estimates the impacts to agricultural firms and consumers, quantifies costs to non-agricultural activities in the epidemiologically impacted region, and(More)
A generalized maximum entropy estimator is developed for the linear simultaneous equations model. Monte Carlo sampling experiments are used to evaluate the estimator's performance in small and medium sized samples, suggesting contexts in which the current generalized maximum entropy estimator is superior in mean square error to two and three stage least(More)
Information theoretic estimators for the first-order spatial autoregressive model are introduced, small sample properties are investigated, and the estimator is applied empirically. Monte Carlo experiments are used to compare finite sample performance of more traditional spatial estimators to three different information theoretic estimators, including(More)
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