7 papers · 1 filter
Shape-constrained density estimation with Wasserstein projection
Takeru Matsuda, Ting-Kam Leonard Wong
Statistical inference based on optimal transport offers a different perspective from that of maximum likelihood, and has increasingly gained attention in recent years. In this pape…
Asymptotic testing of covariance separability for matrix elliptical data
Joni Virta, Takeru Matsuda
We propose a new asymptotic test for the separability of a covariance matrix. The null distribution is valid in wide matrix elliptical model that includes, in particular, both matr…
Flatness of location-scale-shape models under the Wasserstein metric
Ayumu Fukushi, Yoshinori Nakanishi-Ohno, Takeru Matsuda
In Wasserstein geometry, one-dimensional location-scale models are flat both intrinsically and extrinsically-that is, they are curvature-free as well as totally geodesic in the spa…
Wasserstein projection estimators for circular distributions
Naoki Otani, Takeru Matsuda
For statistical models on circles, we investigate performance of estimators defined as the projections of the empirical distribution with respect to the Wasserstein distance. We de…
On the attainment of the Wasserstein--Cramer--Rao lower bound
Hayato Nishimori, Takeru Matsuda
Recently, a Wasserstein analogue of the Cramer--Rao inequality has been developed using the Wasserstein information matrix (Otto metric). This inequality provides a lower bound on…
Priors for second-order unbiased Bayes estimators
Mana Sakai, Takeru Matsuda, Tatsuya Kubokawa
Asymptotically unbiased priors, introduced by Hartigan (1965), are designed to achieve second-order unbiasedness of Bayes estimators. This paper extends Hartigan's framework to non…