20 citations · 68 across the 28 of their papers we have counts for
29 papers
Quantifying uncertainty in the numerical integration of evolution equations based on Bayesian isotonic regression
Yuto Miyatake, Kaoru Irie, Takeru Matsuda
This paper presents a new Bayesian framework for quantifying discretization errors in numerical solutions of ordinary differential equations. By modelling the errors as random vari…
Matrix norm shrinkage estimators and priors
Xiao Li, Takeru Matsuda, Fumiyasu Komaki
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…
Double shrinkage priors for a normal mean matrix
Takeru Matsuda, Fumiyasu Komaki, William E. Strawderman
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, wh…
Minimaxity under half-Cauchy type priors
Yuzo Maruyama, Takeru Matsuda
This is a follow-up paper of Polson and Scott (2012, Bayesian Analysis), which claimed that the half-Cauchy prior is a sensible default prior for a scale parameter in hierarchical…
Information Geometry of Wasserstein Statistics on Shapes and Affine Deformations
Shun-ichi Amari, Takeru Matsuda
Information geometry and Wasserstein geometry are two main structures introduced in a manifold of probability distributions, and they capture its different characteristics. We stud…
Modelling the discretization error of initial value problems using the Wishart distribution
Naoki Marumo, Takeru Matsuda, Yuto Miyatake
This paper presents a new discretization error quantification method for the numerical integration of ordinary differential equations. The error is modelled by using the Wishart di…
Matrix quadratic risk of orthogonally invariant estimators for a normal mean matrix
Takeru Matsuda
In estimation of a normal mean matrix under the matrix quadratic loss, we develop a general formula for the matrix quadratic risk of orthogonally invariant estimators. The derivati…
Inadmissibility of the corrected Akaike information criterion
Takeru Matsuda
For the multivariate linear regression model with unknown covariance, the corrected Akaike information criterion is the minimum variance unbiased estimator of the expected Kullback…
Asymptotic mixed normality of maximum likelihood estimator for Ewens--Pitman partition
Takuya Koriyama, Takeru Matsuda, Fumiyasu Komaki
This paper investigates the asymptotic properties of parameter estimation for the Ewens--Pitman partition with parameters and . Especially, we show that the maximum l…
Adapting to general quadratic loss via singular value shrinkage
Takeru Matsuda
The Gaussian sequence model is a canonical model in nonparametric estimation. In this study, we introduce a multivariate version of the Gaussian sequence model and investigate adap…
Piecewise monotone estimation in one-parameter exponential family
Takeru Matsuda, Yuto Miyatake
The problem of estimating a piecewise monotone sequence of normal means is called the nearly isotonic regression. For this problem, an efficient algorithm has been devised by modif…
Interpretable Stein Goodness-of-fit Tests on Riemannian Manifolds
Wenkai Xu, Takeru Matsuda
In many applications, we encounter data on Riemannian manifolds such as torus and rotation groups. Standard statistical procedures for multivariate data are not applicable to such…
Wasserstein Statistics in One-dimensional Location-Scale Model
Shun-ichi Amari, Takeru Matsuda
Wasserstein geometry and information geometry are two important structures to be introduced in a manifold of probability distributions. Wasserstein geometry is defined by using the…
Estimation under matrix quadratic loss and matrix superharmonicity
Takeru Matsuda, William E. Strawderman
We investigate estimation of a normal mean matrix under the matrix quadratic loss. Improved estimation under the matrix quadratic loss implies improved estimation of any linear com…
Information geometry of operator scaling
Takeru Matsuda, Tasuku Soma
Matrix scaling is a classical problem with a wide range of applications. It is known that the Sinkhorn algorithm for matrix scaling is interpreted as alternating e-projections from…
Generalization of partitioned Runge--Kutta methods for adjoint systems
Takeru Matsuda, Yuto Miyatake
This study computes the gradient of a function of numerical solutions of ordinary differential equations (ODEs) with respect to the initial condition. The adjoint method computes t…
A Stein Goodness-of-fit Test for Directional Distributions
Wenkai Xu, Takeru Matsuda
In many fields, data appears in the form of direction (unit vector) and usual statistical procedures are not applicable to such directional data. In this study, we propose non-para…
Adjoint-based exact Hessian computation
Shin-ichi Ito, Takeru Matsuda, Yuto Miyatake
We consider a scalar function depending on a numerical solution of an initial value problem, and its second-derivative (Hessian) matrix for the initial value. The need to extract t…
Estimation of ordinary differential equation models with discretization error quantification
Takeru Matsuda, Yuto Miyatake
We consider parameter estimation of ordinary differential equation (ODE) models from noisy observations. For this problem, one conventional approach is to fit numerical solutions (…
Information criteria for non-normalized models
Takeru Matsuda, Masatoshi Uehara, Aapo Hyvarinen
Many statistical models are given in the form of non-normalized densities with an intractable normalization constant. Since maximum likelihood estimation is computationally intensi…
Predictive density estimation under the Wasserstein loss
Takeru Matsuda, William E. Strawderman
We investigate predictive density estimation under the Wasserstein loss for location families and location-scale families. We show that plug-in densities form a complete clas…
Imputation estimators for unnormalized models with missing data
Masatoshi Uehara, Takeru Matsuda, Jae Kwang Kim
Several statistical models are given in the form of unnormalized densities, and calculation of the normalization constant is intractable. We propose estimation methods for such unn…
Unified estimation framework for unnormalized models with statistical efficiency
Masatoshi Uehara, Takafumi Kanamori, Takashi Takenouchi +1
The parameter estimation of unnormalized models is a challenging problem. The maximum likelihood estimation (MLE) is computationally infeasible for these models since normalizing c…
Analysis of Noise Contrastive Estimation from the Perspective of Asymptotic Variance
Masatoshi Uehara, Takeru Matsuda, Fumiyasu Komaki
There are many models, often called unnormalized models, whose normalizing constants are not calculated in closed form. Maximum likelihood estimation is not directly applicable to…