Data augmentation strategies for the Bayesian spatial probit regression model

Berrett, Candace; Calder, Catherine A.

doi:10.1016/j.csda.2011.08.020

Cited by 23 publications

(26 citation statements)

References 21 publications

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“…The PX-PDA strategy is once again a hybrid Gibbs sampler with Metropolis-Hastings steps. For clarity, we diverge from the previously used labels 'marginal' and 'conditional data augmentation' (Imai and Van Dyk, 2005;Berrett and Calder, 2012) since our sampling scheme is a combination of both data augmentation and parameter expansion. However, where possible, we will make connections between our algorithm and their marginal augmentation schemes.…”

Section: Parameter-expanded Data Augmentationmentioning

confidence: 98%

“…We extend the algorithm they deem optimal in terms of convergence and autocorrelation. We develop a parameter-expanded algorithm that both enhances our PDA algorithm (A.3) and extends the work of Imai and Van Dyk (2005) and Berrett and Calder (2012) to ordinal, spatial response data.…”

Section: Parameter-expanded Data Augmentationmentioning

confidence: 99%

“…Therefore, we modify the framework of Imai and Van Dyk (2005) and Berrett and Calder (2012) to develop an algorithm for ordinal data.…”

Section: Parameter-expanded Data Augmentationmentioning

confidence: 99%

“…Imai and Van Dyk (2005) present a set of data augmentation and parameter-expanded data augmentation (PX-DA) schemes for multinomial response data. Berrett and Calder (2012) build upon Imai and Van Dyk (2005) to handle spatially correlated binary data. In this work, we explore various PX-DA approaches for ordinal data.…”

Section: Parameter-expanded Data Augmentationmentioning

confidence: 99%

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Data augmentation and parameter expansion for independent or spatially correlated ordinal data

Schliep

Hoeting

2015

Computational Statistics & Data Analysis

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Section: Parameter-expanded Data Augmentationmentioning

confidence: 98%

Section: Parameter-expanded Data Augmentationmentioning

confidence: 99%

“…Therefore, we modify the framework of Imai and Van Dyk (2005) and Berrett and Calder (2012) to develop an algorithm for ordinal data.…”

Section: Parameter-expanded Data Augmentationmentioning

confidence: 99%

Section: Parameter-expanded Data Augmentationmentioning

confidence: 99%

See 2 more Smart Citations

Data augmentation and parameter expansion for independent or spatially correlated ordinal data

Schliep

Hoeting

2015

Computational Statistics & Data Analysis

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“…Considering a similar framework, [6] also provided composite likelihood approach. [2] demonstrated how the non-identifiable spatial variance parameter can be used to create data augmentation MCMC algorithms in Bayesian probit regression model.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Prediction in Clipped GLG Random Field Using Slice Sampling

Khaledi¹,

Zareifard²,

Rivaz³

2014

JSTA

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By assuming that an underlying Gaussian-Log Gaussian (GLG) random field clipped to yield binary spatial data, we propose a new model which provides flexibility in capturing the effects of heavy tail in latent variables. For our analysis, we adopt a Bayesian framework and develop a Markov chain Monte Carlo (MCMC) algorithm to carry out the posterior computations. Specifically, we introduce auxiliary variables and employ the slice sampling method to simulate from the full conditional distribution of components which does not define a standard probability distribution. Then, the predictive distribution at unsampled sites is approximated based on acquired samples. Finally, we illustrate our methodology considering simulation and real data sets.

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Constructing valid spatial processes on the sphere using kernel convolutions

et al. 2014

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Remotely sensed data products are now routinely used to study various aspects of the Earth's atmosphere. These remote sensing datasets are typically very high dimensional, have near global coverage and exhibit nonstationary spatial correlation structures. Proper statistical analysis of these datasets should be sufficiently flexible to account for all these aspects. To this end, we develop a kernel convolution construction of spatial processes on a sphere. As is the case with kernel convolution constructions on the plane, we establish a link between stationary kernels and a stationary covariance function on the sphere via the spherical harmonic decomposition of the kernel. We also introduce the Kent distribution as an appropriate kernel with interpretable parameters to be used in the kernel convolution construction. We demonstrate the discrete kernel convolution model using a dataset of remotely sensed CO 2 concentrations over the globe.

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Data augmentation strategies for the Bayesian spatial probit regression model

Cited by 23 publications

References 21 publications

Data augmentation and parameter expansion for independent or spatially correlated ordinal data

Data augmentation and parameter expansion for independent or spatially correlated ordinal data

Bayesian Prediction in Clipped GLG Random Field Using Slice Sampling

Constructing valid spatial processes on the sphere using kernel convolutions

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