Zobrazeno 1 - 10
of 225
pro vyhledávání: '"Irie Kaoru"'
Shrinkage priors are a popular Bayesian paradigm to handle sparsity in high-dimensional regression. Still limited, however, is a flexible class of shrinkage priors to handle grouped sparsity, where covariates exhibit some natural grouping structure.
Externí odkaz:
http://arxiv.org/abs/2412.15293
This paper presents a new Bayesian framework for quantifying discretization errors in numerical solutions of ordinary differential equations. By modelling the errors as random variables, we impose a monotonicity constraint on the variances, referred
Externí odkaz:
http://arxiv.org/abs/2411.08338
Autor:
Masuda, Riku, Irie, Kaoru
Bayesian predictive synthesis is useful in synthesizing multiple predictive distributions coherently. However, the proof for the fundamental equation of the synthesized predictive density has been missing. In this technical report, we review the seri
Externí odkaz:
http://arxiv.org/abs/2409.09660
Functional time series data frequently appears in econometric analyses, where the functions of interest are subject to some shape constraints, including monotonicity and convexity, as typical of the estimation of the Lorenz curve. This paper proposes
Externí odkaz:
http://arxiv.org/abs/2404.07586
Autor:
Han, Geonhee, Irie, Kaoru
The disaggregated time-series data for Consumer Price Index often exhibits frequent instances of exact zero price changes, stemming from measurement errors inherent in the data collection process. However, the currently prominent stochastic volatilit
Externí odkaz:
http://arxiv.org/abs/2403.10945
Autor:
Masuda, Riku, Irie, Kaoru
Dynamic Bayesian predictive synthesis is a formal approach to coherently synthesizing multiple predictive distributions into a single distribution. In sequential analysis, the computation of the synthesized predictive distribution has heavily relied
Externí odkaz:
http://arxiv.org/abs/2308.15910
Robust Bayesian linear regression is a classical but essential statistical tool. Although novel robustness properties of posterior distributions have been proved recently under a certain class of error distributions, their sufficient conditions are r
Externí odkaz:
http://arxiv.org/abs/2303.00281
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 efficient sampling scheme for the MGIG distributions has not been fully
Externí odkaz:
http://arxiv.org/abs/2302.09707
Publikováno v:
Scandinavian Journal of Statistics, 2024
Isotonic regression or monotone function estimation is a problem of estimating function values under monotonicity constraints, which appears naturally in many scientific fields. This paper proposes a new Bayesian method with global-local shrinkage pr
Externí odkaz:
http://arxiv.org/abs/2208.05121
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 in full conditional densities. Our approach is to approximate full co
Externí odkaz:
http://arxiv.org/abs/2203.01704