1 citations · 1 across the 13 of their papers we have counts for
13 papers
Convergence fragility in probit Bayesian kernel machine regression implemented in the bkmr R package for binary-outcome environmental mixture analyses: a simulation study
Akifumi Eguchi, Takayuki Kawashima, Tomotaka Momozaki +1
Background. Bayesian kernel machine regression (BKMR) is widely used for exposure-mixture analyses with binary outcomes through a probit extension. Because a bkmr fit can complete…
Robust Spatio-Temporal Distributional Regression
Tomotaka Momozaki, Shonosuke Sugasawa, Tomoyuki Nakagawa +2
Motivated by investigating spatio-temporal patterns of the distribution of continuous variables, we consider describing the conditional distribution function of the response variab…
Bayesian Doubly Robust Causal Inference via Posterior Coupling
Shunichiro Orihara, Tomotaka Momozaki, Shonosuke Sugasawa
Bayesian doubly robust (DR) causal inference faces a fundamental dilemma: joint modeling of outcome and propensity score suffers from the feedback problem where outcome information…
On window mean survival time with interval-censored data
Takuto Iijima, Tomotaka Momozaki, Shuji Ando
In recent years, cancer clinical trials have increasingly encountered non proportional hazards (NPH) scenarios, particularly with the emergence of immunotherapy. In randomized cont…
Improving Maximum Tolerated Dose Selection in Model-Assisted Designs for Phase I Trials through Bayesian Dose-Response Model
Rentaro Wakayama, Tomotaka Momozaki, Shuji Ando
Model-assisted designs have garnered significant attention in recent years due to their high accuracy in identifying the maximum tolerated dose (MTD) and their operational simplici…
Sampling from density power divergence-based generalized posterior distribution via stochastic optimization
Naruki Sonobe, Tomotaka Momozaki, Tomoyuki Nakagawa
Robust Bayesian inference using density power divergence (DPD) has emerged as a promising approach for handling outliers in statistical estimation. Although the DPD-based posterior…
Bayesian-based Propensity Score Subclassification Estimator
Shunichiro Orihara, Tomotaka Momozaki
Subclassification estimators are one of the methods used to estimate causal effects of interest using the propensity score. This method is more stable compared to other weighting m…
General Bayesian inference for causal effects using covariate balancing procedure
Shunichiro Orihara, Tomotaka Momozaki, Tomoyuki Nakagawa
In observational studies, the propensity score plays a central role in estimating causal effects of interest. The inverse probability weighting (IPW) estimator is commonly used for…
Semiparametric Copula Estimation for Spatially Correlated Multivariate Mixed Outcomes: Analyzing Visual Sightings of Fin Whales from a Line Transect Survey
Tomotaka Momozaki, Tomoyuki Nakagawa, Shonosuke Sugasawa +1
For marine biologists, ascertaining the dependence structures between marine species and marine environments, such as sea surface temperature and ocean depth, is imperative for def…
Robustness of Bayesian ordinal response model against outliers via divergence approach
Tomotaka Momozaki, Tomoyuki Nakagawa
Ordinal response model is a popular and commonly used regression for ordered categorical data in a wide range of fields such as medicine and social sciences. However, it is empiric…
Robustness against outliers in ordinal response model via divergence approach
Tomotaka Momozaki, Tomoyuki Nakagawa
This study deals with the problem of outliers in ordinal response model, which is a regression on ordered categorical data as the response variable. ``Outlier" means that the combi…
Generalized Cramér's coefficient via -divergence for contingency tables
Wataru Urasaki, Tomoyuki Nakagawa, Tomotaka Momozaki +1
Various measures in two-way contingency table analysis have been proposed to express the strength of association between row and column variables in contingency tables. Tomizawa et…
Improving the accuracy of estimating indexes in contingency tables using Bayesian estimators
Tomotaka Momozaki, Koji Cho, Tomoyuki Nakagawa +1
In contingency table analysis, one is interested in testing whether a model of interest (e.g., the independent or symmetry model) holds using goodness-of-fit tests. When the null h…