Zobrazeno 1 - 10
of 209
pro vyhledávání: '"Komaki, Fumiyasu"'
We develop a class of minimax estimators for a normal mean matrix under the Frobenius loss, which generalizes the James--Stein and Efron--Morris estimators. It shrinks the Schatten norm towards zero and works well for low-rank matrices. We also propo
Externí odkaz:
http://arxiv.org/abs/2406.06137
Autor:
Sun, Xiyang, Komaki, Fumiyasu
Networks are one of the most valuable data structures for modeling problems in the real world. However, the most recent node embedding strategies have focused on undirected graphs, with limited attention to directed graphs, especially directed hetero
Externí odkaz:
http://arxiv.org/abs/2311.14404
We consider estimation of a normal mean matrix under the Frobenius loss. Motivated by the Efron--Morris estimator, a generalization of Stein's prior has been recently developed, which is superharmonic and shrinks the singular values towards zero. The
Externí odkaz:
http://arxiv.org/abs/2311.13137
Autor:
Ando, Ryo, Komaki, Fumiyasu
When multiple models are considered in regression problems, the model averaging method can be used to weigh and integrate the models. In the present study, we examined how the goodness-of-prediction of the estimator depends on the dimensionality of e
Externí odkaz:
http://arxiv.org/abs/2308.09476
Autor:
Okudo, Michiko, Komaki, Fumiyasu
We investigate predictive densities for multivariate normal models with unknown mean vectors and known covariance matrices. Bayesian predictive densities based on shrinkage priors often have complex representations, although they are effective in var
Externí odkaz:
http://arxiv.org/abs/2212.03444
Autor:
Li, Xiao, Komaki, Fumiyasu
The problem of predicting independent Poisson random variables is commonly encountered in real-life practice. Simultaneous predictive distributions for independent Poisson observables are investigated, and the performance of predictive distributions
Externí odkaz:
http://arxiv.org/abs/2209.14618
We derive the exact asymptotic distribution of the maximum likelihood estimator $(\hat{\alpha}_n, \hat{\theta}_n)$ of $(\alpha, \theta)$ for the Ewens--Pitman partition in the regime of $0<\alpha<1$ and $\theta>-\alpha$: we show that $\hat{\alpha}_n$
Externí odkaz:
http://arxiv.org/abs/2207.01949
Publikováno v:
In Computational Statistics and Data Analysis September 2024 197
Autor:
Yamanaka, Yohta, Kurata, Sumito, Yano, Keisuke, Komaki, Fumiyasu, Shiina, Takahiro, Kato, Aitaro
Publikováno v:
Earth, Planets and Space volume 74, Article number: 43 (2022)
We propose a local earthquake tomography method that applies a structured regularization technique to determine sharp changes in Earth's seismic velocity structure using arrival time data of direct waves. Our approach focuses on the ability to better
Externí odkaz:
http://arxiv.org/abs/2104.09067
Autor:
Oda, Hidemasa, Komaki, Fumiyasu
The prediction of the variance-covariance matrix of the multivariate normal distribution is important in the multivariate analysis. We investigated Bayesian predictive distributions for Wishart distributions under the Kullback-Leibler divergence. The
Externí odkaz:
http://arxiv.org/abs/2101.04919