117 citations · 191 across the 25 of their papers we have counts for
27 papers
Fast boundary-aware spatial intensity estimation on complex domains
Takumi Nakagawa, Kōsaku 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…
An Entropy-Energy Identity for Predictive Kullback-Leibler Regret in Infinitely Divisible Location Models
Kōsaku Takanashi, Kenichiro McAlinn
We consider predictive density estimation under logarithmic score for -dimensional infinitely divisible location models. Taking the formal Bayes predictive density under the Leb…
Predictive Synthesis under Sporadic Participation: Evidence from Inflation Density Surveys
Matthew C. Johnson, Matteo Luciani, Minzhengxiong Zhang +1
Central banks rely on density forecasts from professional surveys to assess inflation risks and communicate uncertainty. A central challenge in using these surveys is irregular par…
When Is Generalized Bayes Bayesian? A Decision-Theoretic Characterization of Loss-Based Updating
Kenichiro McAlinn, Kōsaku 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, Kōsaku 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, Kōsaku 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…
Ensemble Doubly Robust Bayesian Inference via Regression Synthesis
Kaoru Babasaki, Shonosuke Sugasawa, Kosaku Takanashi +1
The doubly robust estimator, which models both the propensity score and outcomes, is a popular approach to estimate the average treatment effect in the potential outcome setting. T…
Inadmissibility and Transience
Kosaku Takanashi, Kenichiro McAlinn
We discuss the relation between the statistical question of inadmissibility and the probabilistic question of transience. Brown (1971) proved the mathematical link between the admi…
Asymptotically Unbiased Synthetic Control Methods by Moment Matching
Masahiro Kato, Akari Ohda
Synthetic Control Methods (SCMs) have become a fundamental tool for comparative case studies. The core idea behind SCMs is to estimate treatment effects by predicting counterfactua…
Bayesian Causal Synthesis for Meta-Inference on Heterogeneous Treatment Effects
Shonosuke Sugasawa, Kosaku Takanashi, Kenichiro McAlinn +1
The estimation of heterogeneous treatment effects in the potential outcome setting is biased when there exists model misspecification or unobserved confounding. As these biases are…
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…
Policy Choice and Best Arm Identification: Asymptotic Analysis of Exploration Sampling
Kaito Ariu, Masahiro Kato, Junpei Komiyama +2
We consider the "policy choice" problem -- otherwise known as best arm identification in the bandit literature -- proposed by Kasy and Sautmann (2021) for adaptive experimental des…
Learning Causal Models from Conditional Moment Restrictions by Importance Weighting
Masahiro Kato, Masaaki Imaizumi, Kenichiro McAlinn +2
We consider learning causal relationships under conditional moment restrictions. Unlike causal inference under unconditional moment restrictions, conditional moment restrictions po…
Convergence of Computed Dynamic Models with Unbounded Shock
Kenichiro McAlinn, Kosaku Takanashi
This paper studies the asymptotic convergence of computed dynamic models when the shock is unbounded. Most dynamic economic models lack a closed-form solution. As such, approximate…
Controlling False Discovery Rates under Cross-Sectional Correlations
Junpei Komiyama, Masaya Abe, Kei Nakagawa +1
We consider controlling the false discovery rate for testing many time series with an unknown cross-sectional correlation structure. Given a large number of hypotheses, false and m…
The Adaptive Doubly Robust Estimator for Policy Evaluation in Adaptive Experiments and a Paradox Concerning Logging Policy
Masahiro Kato, Shota Yasui, Kenichiro McAlinn
The doubly robust (DR) estimator, which consists of two nuisance parameters, the conditional mean outcome and the logging policy (the probability of choosing an action), is crucial…
Mean-shift least squares model averaging
Kenichiro McAlinn, Kosaku Takanashi
This paper proposes a new estimator for selecting weights to average over least squares estimates obtained from a set of models. Our proposed estimator builds on the Mallows model…
Equivariant online predictions of non-stationary time series
Kōsaku Takanashi, Kenichiro McAlinn
We discuss the finite sample theoretical properties of online predictions in non-stationary time series under model misspecification. To analyze the theoretical predictive properti…
Dynamic Sparse Factor Analysis
Kenichiro McAlinn, Veronika Rockova, Enakshi Saha
Its conceptual appeal and effectiveness has made latent factor modeling an indispensable tool for multivariate analysis. Despite its popularity across many fields, there are outsta…
Large-Scale Dynamic Predictive Regressions
Daniele Bianchi, Kenichiro McAlinn
We develop a novel "decouple-recouple" dynamic predictive strategy and contribute to the literature on forecasting and economic decision making in a data-rich environment. Under th…
Dynamic Mixed Frequency Synthesis for Economic Nowcasting
Kenichiro McAlinn
We develop a novel Bayesian framework for dynamic modeling of mixed frequency data to nowcast quarterly U.S. GDP growth. The introduced framework utilizes foundational Bayesian the…
Multivariate Bayesian Predictive Synthesis in Macroeconomic Forecasting
Kenichiro McAlinn, Knut Are Aastveit, Jouchi Nakajima +1
We develop the methodology and a detailed case study in use of a class of Bayesian predictive synthesis (BPS) models for multivariate time series forecasting. This extends the rece…
Dynamic Variable Selection with Spike-and-Slab Process Priors
Veronika Rockova, Kenichiro McAlinn
We address the problem of dynamic variable selection in time series regression with unknown residual variances, where the set of active predictors is allowed to evolve over time. T…