12 citations · 34 across the 14 of their papers we have counts for
14 papers
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…
Scalable Inference for Space-Time Gaussian Cox Processes
Shinichiro Shirota, Sudipto Banerjee
The log-Gaussian Cox process is a flexible and popular class of point pattern models for capturing spatial and space-time dependence for point patterns. Model fitting requires appr…
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…
Analyzing Car Thefts and Recoveries with Connections to Modeling Origin-Destination Point Patterns
Shinichiro Shirota, Alan E Gelfand, Jorge Mateu
For a given region, we have a dataset composed of car theft locations along with a linked dataset of recovery locations which, due to partial recovery, is a relatively small subset…
Inference for log Gaussian Cox processes using an approximate marginal posterior
Shinichiro Shirota, Alan E. Gelfand
The log Gaussian Cox process is a flexible class of point pattern models for capturing spatial and spatio-temporal dependence for point patterns. Model fitting requires approximati…
Space and circular time log Gaussian Cox processes with application to crime event data
Shinichiro Shirota, Alan E. Gelfand
We view the locations and times of a collection of crime events as a space-time point pattern. So, with either a nonhomogeneous Poisson process or with a more general Cox process,…
Approximate Marginal Posterior for Log Gaussian Cox Processes
Shinichiro Shirota, Alan. E. Gelfand
The log Gaussian Cox process is a flexible class of Cox processes, whose intensity surface is stochastic, for incorporating complex spatial and time structure of point patterns. Th…
Approximate Bayesian Computation and Model Validation for Repulsive Spatial Point Processes
Shinichiro Shirota, Alan. E. Gelfand
In many applications involving spatial point patterns, we find evidence of inhibition or repulsion. The most commonly used class of models for such settings are the Gibbs point pro…