3 citations · 3 across the 14 of their papers we have counts for
14 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…
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…
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…
Denoising Cosine Similarity: A Theory-Driven Approach for Efficient Representation Learning
Takumi Nakagawa, Yutaro Sanada, Hiroki Waida +5
Representation learning has been increasing its impact on the research and practice of machine learning, since it enables to learn representations that can apply to various downstr…
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…
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…
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…
Local Asymptotic Normality of Infinite-Dimensional Concave Extended Linear Models
Kosaku Takanashi
We study local asymptotic normality of M-estimates of convex minimization in an infinite dimensional parameter space. The objective function of M-estimates is not necessary differe…