7 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…
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…