Introducing bootstrap methods to investigate coefficient non-stationarity in spatial regression models

A - Papers appearing in refereed journals

Harris, P., Brunsdon, C., Lu, B., Nakaya, T. and Charlton, M. 2017. Introducing bootstrap methods to investigate coefficient non-stationarity in spatial regression models. Spatial Statistics. 21, pp. 241-261.

AuthorsHarris, P., Brunsdon, C., Lu, B., Nakaya, T. and Charlton, M.
Abstract

In this simulation study, parametric bootstrap methods are introduced to test for spatial non-stationarity in the coefficients of regression models. Such a test can be rather simply conducted by comparing a model such as geographically weighted regression (GWR) as an alternative to a standard linear regression, the null hypothesis. In this study however, three spatially autocorrelated regressions are also used as null hypotheses: (i) a simultaneous autoregressive error model; (ii) a moving average error model; and (iii) a simultaneous autoregressive lag model. This expansion of null hypotheses, allows an investigation as to whether the spatial variation in the coefficients obtained using GWR could be attributed to some other spatial process, rather than one depicting non-stationary relationships. The new test is objectively assessed via a simulation experiment that generates data and coefficients with known multivariate spatial properties, all within the spatial setting of the oft-studied Georgia educational attainment data set. By applying the bootstrap test and associated contextual diagnosticsto pre-specified, area-based, geographical processes, our study

KeywordsGeographically weighted regression; Spatial regression; Hypothesis testing; Collinearity; GWmodel
Year of Publication2017
JournalSpatial Statistics
Journal citation21, pp. 241-261
Digital Object Identifier (DOI)doi:10.1016/j.spasta.2017.07.006
Open accessPublished as non-open access
FunderBiotechnology and Biological Sciences Research Council
National Natural Science Foundation of China
Funder project or codeThe North Wyke Farm Platform [2012-2017]
Output statusPublished
Publication dates
Print29 Jul 2017
Publication process dates
Accepted24 Jul 2017
Copyright licenseCC BY
PublisherElsevier Sci Ltd
ISSN2211-6753

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