J
James P. LeSage
Researcher at Texas State University
Publications - 219
Citations - 13318
James P. LeSage is an academic researcher from Texas State University. The author has contributed to research in topics: Spatial dependence & Spatial econometrics. The author has an hindex of 51, co-authored 219 publications receiving 12096 citations. Previous affiliations of James P. LeSage include University of Toledo & College of Business Administration.
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Book
Introduction to spatial econometrics
TL;DR: In this article, an introduction to spatial econometric models and methods is provided that discusses spatial autoregressive processes that can be used to extend conventional regression models and an applied example that examines the relationship between commuting to work times and transportation mode choice for a sample of 3,110 US counties in the year 2000.
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Spatial econometric modeling of origin-destination flows*
James P. LeSage,R. Kelley Pace +1 more
TL;DR: In this paper, the authors propose spatial weight structures that model dependence among the N OD pairs in a fashion consistent with standard spatial autoregressive models, which is an extension of the spatial regression models described in Anselin (1988).
The Theory and Practice of Spatial Econometrics
TL;DR: This text provides an introduction to spatial econometric theory along with numerous applied illustrations of the models and methods described, and describes the implementation details that greatly enhance understanding and allow users to make intelligent use of these methods in applied settings.
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Language functional magnetic resonance imaging in preoperative assessment of language areas: correlation with direct cortical stimulation.
Franck-Emmanuel Roux,Kader Boulanouar,Jean-Albert Lotterie,Mehdi Mejdoubi,James P. LeSage,Isabelle Berry +5 more
TL;DR: The overall results of this study demonstrated that language f MRI could not be used to make critical surgical decisions in the absence of direct brain mapping, and other acquisition protocols are required for evaluation of the potential role of language fMRI in the accurate detection of essential cortical language areas.
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Spatial Growth Regressions: Model Specification, Estimation and Interpretation
TL;DR: The authors used Bayesian model comparison methods to simultaneously specify both the spatial weight structure and explanatory variables for a spatial growth regression involving 255 NUTS 2 regions across 25 European countries and found that incorporating model uncertainty in conjunction with appropriate parameter interpretation decreased the importance of explanatory variables traditionally thought to exert an important influence on regional income growth rates.