12 citations · 34 across the 14 of their papers we have counts for
8 papers · 1 filter
Conditional validity of prediction-powered inference for spatially dependent finite populations
Shinichiro Shirota, Shinichiro SHirota
Prediction-powered inference (PPI) combines full-frame predictions with a small labelled sample to estimate finite-population regression coefficients. We establish conditions for t…
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
Retail store locations are strategically selected rather than randomly distributed, potentially inducing preferential sampling when the latent spatial factors governing placement a…
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.…
Preferential Sampling for Bivariate Spatial Data
Shinichiro Shirota, Alan E. Gelfand
Preferential sampling provides a formal modeling specification to capture the effect of bias in a set of sampling locations on inference when a geostatistical model is used to expl…
Clarifying species dependence under joint species distribution modeling
Alan E. Gelfand, Shinichiro Shirota
Joint species distribution modeling is attracting increasing attention these days, acknowledging the fact that individual level modeling fails to take into account expected depende…
Conjugate Nearest Neighbor Gaussian Process Models for Efficient Statistical Interpolation of Large Spatial Data
Shinichiro Shirota, Andrew O. Finley, Bruce D. Cook +1
A key challenge in spatial statistics is the analysis for massive spatially-referenced data sets. Such analyses often proceed from Gaussian process specifications that can produce…
Preferential sampling for presence/absence data and for fusion of presence/absence data with presence-only data
Alan. E. Gelfand, Shinichiro Shirota
Presence/absence data and presence-only data are the two customary sources for learning about species distributions over a region. We illuminate the fundamental modeling difference…
Spatial Joint Species Distribution Modeling using Dirichlet Processes
Shinichiro Shirota, Alan E. Gelfand, Sudipto Banerjee
Species distribution models usually attempt to explain presence-absence or abundance of a species at a site in terms of the environmental features (socalled abiotic features) prese…