8 papers
Bayesian spatiotemporal conditional autoregressive model for local temporal variations
Takahiro Onizuka, Shintaro Hashimoto
Spatiotemporal areal data are commonly observed in various fields such including epidemiology, social science, economics and so on. To capture both spatial trends and temporal tren…
Global-local shrinkage priors for modeling random effects in multivariate spatial small area estimation
Shushi Nishina, Takahiro Onizuka, Shintaro Hashimoto
Small area estimation (SAE) plays a central role in survey statistics and epidemiology, providing reliable estimates for domains with limited sample sizes. The multivariate Fay-Her…
Robust Bayesian Inference for Censored Survival Models
Yasuyuki Hamura, Takahiro Onizuka, Shintaro Hashimoto +1
This paper proposes a robust Bayesian accelerated failure time model for censored survival data. We develop a new family of life-time distributions using a scale mixture of the gen…
Robust Bayesian graphical modeling using -divergence
Takahiro Onizuka, Shintaro Hashimoto
Gaussian graphical model is one of the powerful tools to analyze conditional independence between two variables for multivariate Gaussian-distributed observations. When the dimensi…
Bayesian Boundary Trend Filtering
Takahiro Onizuka, Fumiya Iwashige, Shintaro Hashimoto
Estimating boundary curves has many applications such as economics, climate science, and medicine. Bayesian trend filtering has been developed as one of locally adaptive smoothing…
Fast and Locally Adaptive Bayesian Quantile Smoothing using Calibrated Variational Approximations
Takahiro Onizuka, Shintaro Hashimoto, Shonosuke Sugasawa
Quantiles are useful characteristics of random variables that can provide substantial information on distributions compared with commonly used summary statistics such as means. In…
Sparse Bayesian inference on gamma-distributed observations using shape-scale inverse-gamma mixtures
Yasuyuki Hamura, Takahiro Onizuka, Shintaro Hashimoto +1
In various applications, we deal with high-dimensional positive-valued data that often exhibits sparsity. This paper develops a new class of continuous global-local shrinkage prior…
Locally Adaptive Spatial Quantile Smoothing: Application to Monitoring Crime Density in Tokyo
Takahiro Onizuka, Shintaro Hashimoto, Shonosuke Sugasawa
Spatial trend estimation under potential heterogeneity is an important problem to extract spatial characteristics and hazards such as criminal activity. By focusing on quantiles, w…