bayesian hierarchical modeling 1preferential sampling 1retail analytics 1spatial point processes 1survival analysis 1
From the 1 of 3 linked papers with an AI index.
3 papers
stat.ME2026
Design-Based Prediction-Powered Inference for Spatial Data
Shinichiro SHirota
Prediction-powered inference (PPI) combines a wall-to-wall prediction map with a small gold-standard sample to give confidence intervals valid whatever the map's quality. Canonical…
stat.ME2026
Assessing Preferential Sampling in Retail Survival Data: A Bayesian Joint LGCP and Spatial Probit Model for Mini-Supermarket Closure in Tokyo
Akitoshi Kanetaka, Shinichiro Shirota
The paper introduces a Bayesian hierarchical model that jointly uses a log‑Gaussian Cox process for retail store locations and a spatial probit regression for binary survival outco…
stat.ME2025
Fast spatio-temporally varying coefficient modeling with reluctant interaction selection
Daisuke Murakami, Shinichiro Shirota, Seiji Kajita +1
Spatially and temporally varying coefficient (STVC) models are currently attracting attention as a flexible tool to explore the spatio-temporal patterns in regression coefficients.…