Full Citation

Title: Local Marginal Analysis of Spatial Data: A Gaussian Process Regression Approach with Bayesian Model and Kernel Averaging

Citation Type: Book, Section

Publication Year: 2016

ISBN: 978-1-78560-986-2

ISSN:

DOI: 10.1108/S0731-905320160000037018

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Abstract: To demonstrate the effectiveness of this Variable Length Scale (VLS) model in terms of both predictions and local marginal analyses, we employ selected simulations to compare VLS with Geographically Weighted Regression (GWR), which is currently the most popular method for such spatial modeling. In addition, we employ the classical Boston Housing data to compare VLS not only with GWR but also with other well-known spatial regression models that have been applied to this same data. Our main results are to show that VLS not only compares favorably with spatial regression at the aggregate level but is also far more accurate than GWR at the local level.

Url: https://www.emerald.com/insight/content/doi/10.1108/S0731-905320160000037018/full/html

User Submitted?: No

Authors: Dearmon, Jacob; Smith, Tony E.

Editors: Baltagi, Badi H.; Lesage, James P.; Pace, R. Kelley

Pages: 297-342

Volume Title: Spatial Econometrics: Qualitative and Limited Dependent Variables

Publisher: Emerald Group Publishing Limited

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Data Collections: IPUMS NHGIS

Topics: Other

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