Bayesian quantile regression based on the asymmetric Laplace (AL) distribution can be sensitive to extreme observations because of its exponentially decaying tails. We propose a robust error distribution constructed as a finite mixture of the AL distribution and a log-Pareto scale mixture of asymmetric Laplace distribu...
We propose conditional f-modeling (Cf-modeling), a framework for empirical Bayes inference with covariates. A central identity shows that the conditional marginal score function determines not only the posterior mean through Tweedie's formula, but also the posterior moment-generating function, providing a basis for rec...
Multivariate correlated outcomes occur across disciplines, including ecology, social sciences, and psychometrics. This paper focuses on clustering these outcomes across observational units, specifically, finding groups of units with the same ``outcome profile". Our motivation comes from bioregionalization in ecology, w...
Shonosuke Sugasawa, Francis K. C. Hui· 0 citations
This article applies gamma-divergence to the restricted maximum likelihood function of the additive model, based on treating the smoothing coefficients as random effects, which leads to an efficient minorization-maximization algorithm that adaptively downweights the impact of outlying observations via a set of normaliz...
Francis K. C. Hui, Ding Ding, Shonosuke Sugasawa· Statistics and computing· 0 citations
Bayesian quantile regression based on the asymmetric Laplace (AL) distribution can be sensitive to extreme observations because of its exponentially decaying tails. We propose a robust error distribution constructed as a finite mixture of the AL distribution and a log-Pareto scale mixture of asymmetric Laplace distribu...
