6 citations · 17 across the 16 of their papers we have counts for
17 papers
Trace-Class Results for MCMC Algorithms for Student- Regression Models
Yasuyuki Hamura
In this paper, we consider MCMC algorithms for Student- regression models. In three cases, we investigate the efficiency of Markov chains based on the algorithms in terms of whe…
Outlier-Robust Bayesian Multivariate Analysis with Correlation-Intact Sandwich Mixture
Yasuyuki Hamura, Kaoru Irie, Shonosuke Sugasawa
Handling outliers is a fundamental challenge in multivariate data analysis because outliers may distort the structures of correlation or conditional independence. Although robust B…
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…
Geometric Ergodicity of Gibbs Algorithms for a Normal Model With a Global-Local Shrinkage Prior
Yasuyuki Hamura
We consider Gibbs samplers for a normal linear regression model with a global-local shrinkage prior and show that they produce geometrically ergodic Markov chains. First, under the…
A Short Note on the Efficiency of Markov Chains for Bayesian Linear Regression Models with Heavy-Tailed Errors
Yasuyuki Hamura
In this short note, we consider posterior simulation for a linear regression model when the error distribution is given by a scale mixture of multivariate normals. We first show th…
An Approximate Identity Link Function for Bayesian Generalized Linear Models
Yasuyuki Hamura
In this note, we consider using a link function that has heavier tails than the usual exponential link function. We construct efficient Gibbs algorithms for Poisson and Multinomial…
State-Space Modeling of Shape-constrained Functional Time Series
Daichi Hiraki, Yasuyuki Hamura, Kaoru Irie +1
Functional time series data frequently appears in econometric analyses, where the functions of interest are subject to some shape constraints, including monotonicity and convexity,…
Posterior Robustness with Milder Conditions: Contamination Models Revisited
Yasuyuki Hamura, Kaoru Irie, Shonosuke Sugasawa
Robust Bayesian linear regression is a classical but essential statistical tool. Although novel robustness properties of posterior distributions have been proved recently under a c…
Gibbs Sampler for Matrix Generalized Inverse Gaussian Distributions
Yasuyuki Hamura, Kaoru Irie, Shonosuke Sugasawa
Sampling from matrix generalized inverse Gaussian (MGIG) distributions is required in Markov Chain Monte Carlo (MCMC) algorithms for a variety of statistical models. However, an ef…
A Simple Proof of Posterior Robustness
Yasuyuki Hamura
Conditions for Bayesian posterior robustness have been examined in recent literature. However, many of the proofs seem to be long and complicated. In this paper, we first summarize…
Locally Adaptive Bayesian Isotonic Regression using Half Shrinkage Priors
Ryo Okano, Yasuyuki Hamura, Kaoru Irie +1
Isotonic regression or monotone function estimation is a problem of estimating function values under monotonicity constraints, which appears naturally in many scientific fields. Th…
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…
On Data Augmentation for Models Involving Reciprocal Gamma Functions
Yasuyuki Hamura, Kaoru Irie, Shonosuke Sugasawa
In this paper, we introduce a new and efficient data augmentation approach to the posterior inference of the models with shape parameters when the reciprocal gamma function appears…
Robust Bayesian Modeling of Counts with Zero inflation and Outliers: Theoretical Robustness and Efficient Computation
Yasuyuki Hamura, Kaoru Irie, Shonosuke Sugasawa
Count data with zero inflation and large outliers are ubiquitous in many scientific applications. However, posterior analysis under a standard statistical model, such as Poisson or…
Log-Regularly Varying Scale Mixture of Normals for Robust Regression
Yasuyuki Hamura, Kaoru Irie, Shonosuke Sugasawa
Linear regression with the classical normality assumption for the error distribution may lead to an undesirable posterior inference of regression coefficients due to the potential…
Shrinkage with Robustness: Log-Adjusted Priors for Sparse Signals
Yasuyuki Hamura, Kaoru Irie, Shonosuke Sugasawa
We introduce a new class of distributions named log-adjusted shrinkage priors for the analysis of sparse signals, which extends the three parameter beta priors by multiplying an ad…
On Global-local Shrinkage Priors for Count Data
Yasuyuki Hamura, Kaoru Irie, Shonosuke Sugasawa
Global-local shrinkage prior has been recognized as useful class of priors which can strongly shrink small signals towards prior means while keeping large signals unshrunk. Althoug…