8 papers · 1 filter
Fast boundary-aware spatial intensity estimation on complex domains
Takumi Nakagawa, KÅsaku Takanashi, Kenichiro McAlinn +1
Spatial intensity maps are routinely used to summarize point patterns on geographically constrained regions, such as islands, coastlines, watersheds, ecological reserves, and admin…
FlowSDR: Sufficient Dimension Reduction via Conditional Normalizing Flows
Yuexiao Dong, Kenichiro Mcalinn, Edoardo Airoldi +1
Sufficient dimension reduction (SDR) seeks a low-dimensional linear projection of predictors that preserves the conditional distribution of the response. Existing methods target th…
When Is Generalized Bayes Bayesian? A Decision-Theoretic Characterization of Loss-Based Updating
Kenichiro McAlinn, KÅsaku Takanashi
Loss-based updating, including generalized Bayes, Gibbs, and quasi-posteriors, replaces likelihoods by a user-chosen loss and produces a posterior-like distribution via exponential…
Dynamic causal inference with time series data
Tanique Schaffe-Odeleye, KÅsaku Takanashi, Vishesh Karwa +2
We generalize the potential outcome framework to time series with an intervention by defining causal effects on stochastic processes. Interventions in dynamic systems alter not onl…
Optimal Hold-Out Size in Cross-Validation
Kenichiro McAlinn, KÅsaku Takanashi
Cross-validation (CV) is routinely used across the sciences to select models and tune parameters, and the resulting choices are often interpreted as substantive scientific conclusi…
Bayesian Spatial Predictive Synthesis
Danielle Cabel, Shonosuke Sugasawa, Masahiro Kato +2
Due to spatial dependence -- often characterized as complex and non-linear -- model misspecification is a prevalent and critical issue in spatial data analysis and prediction. As t…