Bayesian Spatial Quantile Regression

Reich, Brian J.; Fuentes, Montserrat; Dunson, David B.

doi:10.1198/jasa.2010.ap09237

Cited by 178 publications

(159 citation statements)

References 45 publications

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“…We have also recently seen the combination of methods that were previously separate. For example, a small number of scholars have explored the integration of quantile regression (Koenker 2005) with different spatial techniques, such as simultaneous autoregressive modeling and spatial lag hedonic modeling (Hallin, Lu, and Yu 2009;Kostov 2009;and Su and Yang 2007), M-quantile GWR (Salvati et al 2007), and Bayesian spatially varying coefficient process modeling (Reich, Fuentes, and Dunson 2011). In addition, Chen et al (2012) have developed a geographically weighted quantile regression (GWQR) method and have applied it to the study of spatial inequality and mortality in the United States.…”

Section: Discussion: the Near Future In Spatial Demographymentioning

confidence: 99%

Progress in Spatial Demography

Matthews¹,

Parker²

2013

DemRes

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BACKGROUNDDemography is an inherently spatial science, yet the application of spatial data and methods to demographic research has tended to lag that of other disciplines. In recent years, there has been a surge in interest in adding a spatial perspective to demography. This sharp rise in interest has been driven in part by rapid advances in geospatial data, new technologies, and methods of analysis. OBJECTIVESWe offer a brief introduction to four of the advanced spatial analytic methods: spatial econometrics, geographically weighted regression, multilevel modeling, and spatial pattern analysis. We look at both the methods used and the insights that can be gained by applying a spatial perspective to demographic processes and outcomes. To help illustrate these substantive insights, we introduce six papers that are included in a Special Collection on Spatial Demography. We close with some predictions for the future, as we anticipate that spatial thinking and the use of geospatial data, technology, and analytical methods will change how many demographers address important demographic research questions.

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Section: Discussion: the Near Future In Spatial Demographymentioning

confidence: 99%

Progress in Spatial Demography

Matthews¹,

Parker²

2013

DemRes

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“…Since [11]. Bayesian inference quantile regression has attracted a lot of attention in literature including [4][5][6][7]9,10,[12][13][14][15][16][17][18][19][20][21]. However, almost all these models set priors independent of the values of quantiles, or the prior is the same for modelling different quantiles [27].…”

Section: Power Prior Distributions and Gibbs Samplermentioning

confidence: 99%

Prior Elicitation in Bayesian Quantile Regression for Longitudinal Data

Alhamzawi¹,

Yu²

2011

J Biomet Biostat

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In this paper, we introduce Bayesian quantile regression for longitudinal data in terms of informative priors and Gibbs sampling. We develop methods for eliciting prior distribution to incorporate historical data gathered from similar previous studies. The methods can be used either with no prior data or with complete prior data. The advantage of the methods is that the prior distribution is changing automatically when we change the quantile. We propose Gibbs sampling methods which are computationally efficient and easy to implement. The methods are illustrated with both simulation and real data.

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“…5 and 95%) may be estimated from quantile specific regression, which allows covariate impacts to vary by quantile. The ability to examine central and extreme quantiles in relation to particular covariate combinations may be important for policy formulation or assessment (Reich et al 2011). One may also focus on a lower quantile (such as 2.5 or 5%), and identify probabilities of excess incidence or relative risk at this quantile.…”

Section: Methods: Hierarchical Poisson Log-normalmentioning

confidence: 99%

Quantile regression for overdispersed count data: a hierarchical method

Congdon

2017

J Stat Distrib App

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Generalized Poisson regression is commonly applied to overdispersed count data, and focused on modelling the conditional mean of the response. However, conditional mean regression models may be sensitive to response outliers and provide no information on other conditional distribution features of the response. We consider instead a hierarchical approach to quantile regression of overdispersed count data. This approach has the benefits of effective outlier detection and robust estimation in the presence of outliers, and in health applications, that quantile estimates can reflect risk factors. The technique is first illustrated with simulated overdispersed counts subject to contamination, such that estimates from conditional mean regression are adversely affected. A real application involves ambulatory care sensitive emergency admissions across 7518 English patient general practitioner (GP) practices. Predictors are GP practice deprivation, patient satisfaction with care and opening hours, and region. Impacts of deprivation are particularly important in policy terms as indicating effectiveness of efforts to reduce inequalities in care sensitive admissions. Hierarchical quantile count regression is used to develop profiles of central and extreme quantiles according to specified predictor combinations.

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Bayesian Spatial Quantile Regression

Cited by 178 publications

References 45 publications

Progress in Spatial Demography

Progress in Spatial Demography

Prior Elicitation in Bayesian Quantile Regression for Longitudinal Data

Quantile regression for overdispersed count data: a hierarchical method

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