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Heterogeneous Regression Models for Clusters of Spatial Dependent Data

Zhihua Ma, Yishu Xue, Guanyu Hu

arXiv 4 Jul 2019 · Econometrics · publishedSpatial Economic Analysis (2020) · 20 citations (OpenAlex)

arXiv:1907.02212 · PDF · DOI · OpenAlex · Extracted main text

Abstract

In economic development, there are often regions that share similar economic characteristics, and economic models on such regions tend to have similar covariate effects. In this paper, we propose a Bayesian clustered regression for spatially dependent data in order to detect clusters in the covariate effects. Our proposed method is based on the Dirichlet process which provides a probabilistic framework for simultaneous inference of the number of clusters and the clustering configurations. The usage of our method is illustrated both in simulation studies and an application to a housing cost dataset of Georgia.

Citation extraction

31
references
102
in-text mentions
84
distinct cited
5
self-citations
8,641
main-text words

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Gelfand, A. E., H.-J. Kim, C. Sirmans, and S. Banerjee (2003) Spatial modeling with spatially varying coefficient processes0.73732100%
Xueunmatched citation key Xue0.64441100%
and Huunmatched citation key and Hu0.64441100%
4de Valpine, P., D. Turek, C. J. Paciorek, C. Anderson-Bergman, D. T.… (2017) Programming with models: writing statistical algorithms for general model structures with NIMBLE0.64422100%
5Ibrahim, J. G., M.-H. Chen, and D. Sinha (2013) Bayesian Survival Analysis0.64422100%
Gelfandunmatched citation key Gelfand0.51121100%
Gengunmatched citation key Geng0.51121100%
Maunmatched citation key Ma0.51121100%
and Banerjeeunmatched citation key and Banerjee0.51121100%
10Brunsdon, C., A. S. Fotheringham, and M. E. Charlton (1996) Geographically weighted regression: a method for exploring spatial nonstationarity0.51121100%

Showing the top 10 of 84 scored citations. 6 of these could not be matched to a bibliography entry, so only the citation key is shown.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Bayesian Clustered Coefficients Regression with Auxiliary Covariates Assistant Random Effects0.84333