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Bayesian Clustered Coefficients Regression with Auxiliary Covariates Assistant Random Effects

Guanyu Hu, Yishu Xue, Zhihua Ma

arXiv 25 Apr 2020 · Statistics — Methodology · publishedStatistical Modelling (2021) · 2 citations (OpenAlex)

arXiv:2004.12022 · PDF · DOI · OpenAlex · Extracted main text

Abstract

In regional economics research, a problem of interest is to detect similarities between regions, and estimate their shared coefficients in economics models. In this article, we propose a mixture of finite mixtures (MFM) clustered regression model with auxiliary covariates that account for similarities in demographic or economic characteristics over a spatial domain. Our Bayesian construction provides both inference for number of clusters and clustering configurations, and estimation for parameters for each cluster. Empirical performance of the proposed model is illustrated through simulation experiments, and further applied to a study of influential factors for monthly housing cost in Georgia.

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46
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55
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46
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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
1Ma, Z., Xue, Y., and Hu, G (2020) Heterogeneous regression models for clusters of spatial dependent data self0.84333100%
2Zou, T., Lan, W., Wang, H., and Tsai, C.-L (2017) Covariance regression analysis0.84333100%
3Miller, J. W. and Harrison, M. T (2018) Mixture models with a prior on the number of components0.73732100%
4Gelfand, A. E., Kim, H.-J., Sirmans, C., and Banerjee, S (2003) Spatial modeling with spatially varying coefficient processes0.64422100%
5Miller, J. W. and Harrison, M. T (2013) A simple example of Dirichlet process mixture inconsistency for the number of components0.64422100%
6Neal, R. M (2000) Markov chain sampling methods for Dirichlet process mixture models0.64422100%
7Vavrek, M. J (2011) fossil: Palaeoecological and palaeogeographical analysis tools0.40511100%
8Bradley, J. R., Holan, S. H., Wikle, C. K., et al (2018) Computationally efficient multivariate spatio-temporal models for high-dimensional count-valued data (with discussion)0.40511100%
9Brunsdon, C., Fotheringham, A. S., and Charlton, M. E (1996) Geographically weighted regression: a method for exploring spatial nonstationarity0.40511100%
10Carlin, B. P., Gelfand, A. E., and Banerjee, S (2014) Hierarchical Modeling and Analysis for Spatial Data0.40511100%

Showing the top 10 of 46 scored citations.