4 papers · 1 filter
Log-Regularly Varying Scale Mixture of Normals for Robust Regression
Yasuyuki Hamura, Kaoru Irie, Shonosuke Sugasawa
Linear regression with the classical normality assumption for the error distribution may lead to an undesirable posterior inference of regression coefficients due to the potential…
Shrinkage with Robustness: Log-Adjusted Priors for Sparse Signals
Yasuyuki Hamura, Kaoru Irie, Shonosuke Sugasawa
We introduce a new class of distributions named log-adjusted shrinkage priors for the analysis of sparse signals, which extends the three parameter beta priors by multiplying an ad…
On Global-local Shrinkage Priors for Count Data
Yasuyuki Hamura, Kaoru Irie, Shonosuke Sugasawa
Global-local shrinkage prior has been recognized as useful class of priors which can strongly shrink small signals towards prior means while keeping large signals unshrunk. Althoug…
Bayesian Dynamic Fused LASSO
Kaoru Irie
The new class of Markov processes is proposed to realize the flexible shrinkage effects for the dynamic models. The transition density of the new process consists of two penalty fu…