Zobrazeno 1 - 10
of 89
pro vyhledávání: '"Hamura, Yasuyuki"'
Functional time series data frequently appears in economic applications, 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 propose
Externí odkaz:
http://arxiv.org/abs/2404.07586
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
Autor:
Hamura, Yasuyuki
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 some basic lemmas that have been applied implicitly or explicitly. Th
Externí odkaz:
http://arxiv.org/abs/2301.06099
Autor:
Hamura, Yasuyuki
In this short note, we consider the problem of estimating multivariate hypergeometric parameters under squared error loss when side information in aggregated data is available. We use the symmetric multinomial prior to obtain Bayes estimators. It is
Externí odkaz:
http://arxiv.org/abs/2208.10975
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
Sparse Bayesian inference on gamma-distributed observations using shape-scale inverse-gamma mixtures
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 priors tailored to analyzing gamma-distributed observations where most of t
Externí odkaz:
http://arxiv.org/abs/2203.08440
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
Autor:
Hamura, Yasuyuki
In this paper, we consider simultaneous estimation of Poisson parameters in situations where we can use side information in aggregated data. We use standardized squared error and entropy loss functions. Bayesian shrinkage estimators are derived based
Externí odkaz:
http://arxiv.org/abs/2112.13245
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 negative binomial distribution, is sensitive to such contamination. T
Externí odkaz:
http://arxiv.org/abs/2106.10503