9 papers
Empirical Bayes 1-bit matrix completion
Takeru Matsuda
The problem of predicting unobserved entries in a binary matrix, known as 1-bit matrix completion, has found diverse applications in fields such as recommendation systems. In this…
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…
Contrastive Bayesian Inference for Unnormalized Models
Naruki Sonobe, Shonosuke Sugasawa, Daichi Mochihashi +1
Unnormalized (or energy-based) models provide a flexible framework for capturing the characteristics of data with complex dependency structures. However, the application of standar…
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…