1 citations · 1 across the 14 of their papers we have counts for
8 papers · 1 filter
On Misspecified Error Distributions in Bayesian Functional Clustering: Consequences and Remedies
Fumiya Iwashige, Tomoya Wakayama, Shonosuke Sugasawa +1
Nonparametric Bayesian approaches provide a flexible framework for clustering without pre-specifying the number of groups, yet they are well known to overestimate the number of clu…
Clustered Factor Analysis for Multivariate Spatial Data
Yanxiu Jin, Tomoya Wakayama, Renhe Jiang +1
Factor analysis has been extensively used to reveal the dependence structures among multivariate variables, offering valuable insight in various fields. However, it cannot incorpor…
Ensemble Prediction via Covariate-dependent Stacking
Tomoya Wakayama, Shonosuke Sugasawa
This study proposes a novel approach to ensemble prediction, called "covariate-dependent stacking" (CDST). Unlike traditional stacking and model averaging methods, CDST allows mode…
Process-based Inference for Spatial Energetics Using Bayesian Predictive Stacking
Tomoya Wakayama, Sudipto Banerjee
Rapid developments in streaming data technologies have enabled real-time monitoring of human activity that can deliver high-resolution data on health variables over trajectories or…
Reconciling Functional Data Regression with Excess Bases
Tomoya Wakayama, Hidetoshi Matsui
As the development of measuring instruments and computers has accelerated the collection of massive amounts of data, functional data analysis (FDA) has experienced a surge of atten…
Similarity-based Random Partition Distribution for Clustering Functional Data
Tomoya Wakayama, Shonosuke Sugasawa, Genya Kobayashi
Random partition distribution is a crucial tool for model-based clustering. This study advances the field of random partition in the context of functional spatial data, focusing on…
Functional Horseshoe Smoothing for Functional Trend Estimation
Tomoya Wakayama, Shonosuke Sugasawa
Due to developments in instruments and computers, functional observations are increasingly popular. However, effective methodologies for flexibly estimating the underlying trends w…
Trend Filtering for Functional Data
Tomoya Wakayama, Shonosuke Sugasawa
Despite increasing accessibility to function data, effective methods for flexibly estimating underlying functional trend are still scarce. We thereby develop functional version of…