Latent multivariate log-gamma models for high-dimensional multitype responses with application to daily fine particulate matter and mortality counts
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Publication:6104100
DOI10.1214/22-aoas1664arXiv1909.02528MaRDI QIDQ6104100
Zhixing Xu, Debajyoti Sinha, Jonathan R. Bradley
Publication date: 5 June 2023
Published in: The Annals of Applied Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1909.02528
Cites Work
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- A flexible zero-inflated model to address data dispersion
- Multivariate spatio-temporal models for high-dimensional areal data with application to longitudinal employer-household dynamics
- A comparison of spatial predictors when datasets could be very large
- Hierarchical Poisson models for spatial count data
- Multilevel latent Gaussian process model for mixed discrete and continuous multivariate response data
- A general science-based framework for dynamical spatio-temporal models
- Selection between Weibull and lognormal distributions: a comparative simulation study
- Improving the performance of predictive process modeling for large datasets
- Spatial-temporal association between fine particulate matter and daily mortality
- Bayesian image restoration, with two applications in spatial statistics (with discussion)
- Conjugate priors for exponential families
- Slice sampling. (With discussions and rejoinder)
- Inference from iterative simulation using multiple sequences
- Computationally efficient multivariate spatio-temporal models for high-dimensional count-valued data (with discussion)
- Posterior predictive \(p\)-values
- A case study competition among methods for analyzing large spatial data
- Comparing and selecting spatial predictors using local criteria
- Nonseparable dynamic nearest neighbor Gaussian process models for large spatio-temporal data with an application to particulate matter analysis
- Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations
- Estimating causal effects of air quality regulations using principal stratification for spatially correlated multivariate intermediate outcomes
- Selfimprovemvent of the inequality between arithmetic and geometric means
- Spatial Association between Speciated Fine Particles and Mortality
- Fixed Rank Kriging for Very Large Spatial Data Sets
- Latent Variable Analysis of Multivariate Spatial Data
- Combining Incompatible Spatial Data
- A note on the correlation structure of transformed Gaussian random fields
- Case-Control Studies with Errors in Covariates
- A dimension-reduced approach to space-time Kalman filtering
- An Approach to Incorporate Subsampling Into a Generic Bayesian Hierarchical Model
- Dimension Reduction and Alleviation of Confounding for Spatial Generalized Linear Mixed Models
- Bayesian Hierarchical Models With Conjugate Full-Conditional Distributions for Dependent Data From the Natural Exponential Family
- Hierarchical Models for Spatial Data with Errors that are Correlated with the Latent Process
- Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA
- Strictly Proper Scoring Rules, Prediction, and Estimation
- Measurement Error in Nonlinear Models
- Spatio‐temporal smoothing and EM estimation for massive remote‐sensing data sets
- Regionalization of Multiscale Spatial Processes by Using a Criterion for Spatial Aggregation Error
- NOTES ON CONTINUOUS STOCHASTIC PHENOMENA
- Highly Scalable Bayesian Geostatistical Modeling via Meshed Gaussian Processes on Partitioned Domains
- A general framework for Vecchia approximations of Gaussian processes
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