11 citations · 19 across the 13 of their papers we have counts for
13 papers
Developing an information criterion for spatial data analysis through Bayesian generalized fused lasso
Yuko Kakikawa, Yoshiyuki Ninomiya
In the field of spatial data analysis, spatially varying coefficients (SVC) models, which allow regression coefficients to vary by region and flexibly capture spatial heterogeneity…
Akaike information criterion for segmented regression models
Kazuki Nakajima, Yoshiyuki Ninomiya
In segmented regression, when the regression function is continuous at the change-points that are the boundaries of the segments, it is also called joinpoint regression, and the an…
Covariate balancing estimation and model selection for difference-in-differences approach
Takamichi Baba, Yoshiyuki Ninomiya
Remarkable progress has been made in difference-in-differences (DID) approaches to causal inference that estimate the average effect of a treatment on the treated (ATT). Of these,…
Information criteria for detecting change-points in the Cox proportional hazards model
Ryoto Ozaki, Yoshiyuki Ninomiya
The Cox proportional hazards model, commonly used in clinical trials, assumes proportional hazards. However, it does not hold when, for example, there is a delayed onset of the tre…
Information criteria for sparse methods in causal inference
Yoshiyuki Ninomiya
For propensity score analysis and sparse estimation, we develop an information criterion for determining the regularization parameters needed in variable selection. First, for Gaus…
Doubly Robust Criterion for Causal Inference
Takamichi Baba, Yoshiyuki Ninomiya
The semiparametric estimation approach, which includes inverse-probability-weighted and doubly robust estimation using propensity scores, is a standard tool in causal inference, an…
Prior Intensified Information Criterion
Yoshiyuki Ninomiya
The widely applicable information criterion (WAIC) has been used as a model selection criterion for Bayesian statistics in recent years. It is an asymptotically unbiased estimator…
Selective Inference in Propensity Score Analysis
Yoshiyuki Ninomiya, Yuta Umezu, Ichiro Takeuchi
Selective inference (post-selection inference) is a methodology that has attracted much attention in recent years in the fields of statistics and machine learning. Naive inference…
Smoothly varying ridge regularization
Daeju Kim, Shuichi Kawano, Yoshiyuki Ninomiya
A basis expansion with regularization methods is much appealing to the flexible or robust nonlinear regression models for data with complex structures. When the underlying function…
Use of spurious correlation for multiplicity adjustment
Yoshiyuki Ninomiya, Satoshi Kuriki, Toshihiko Shiroishi +1
We consider one of the most basic multiple testing problems that compares expectations of multivariate data among several groups. As a test statistic, a conventional (approximate)…
criterion for semiparametric approach in causal inference
Takamichi Baba, Yoshiyuki Ninomiya
For marginal structural models, which recently play an important role in causal inference, we consider a model selection problem in the framework of a semiparametric approach using…
On the Consistency of the Bias Correction Term of the AIC for the Non-Concave Penalized Likelihood Method
Yuta Umezu, Yoshiyuki Ninomiya
Penalized likelihood methods with an -type penalty, such as the Bridge, the SCAD, and the MCP, allow us to estimate a parameter and to do variable selection, simultaneously…
AIC for Non-concave Penalized Likelihood Method
Yuta Umezu, Yusuke Shimizu, Hiroki Masuda +1
Non-concave penalized maximum likelihood methods, such as the Bridge, the SCAD, and the MCP, are widely used because they not only do parameter estimation and variable selection si…