10 citations · 25 across the 15 of their papers we have counts for
4 papers · 1 filter
An empirical likelihood approach to reduce selection bias in voluntary samples
Jae Kwang Kim, Kosuke Morikawa
We address the weighting problem in voluntary samples under a nonignorable sample selection model. Under the assumption that the sample selection model is correctly specified, we c…
Semiparametric imputation using latent sparse conditional Gaussian mixtures for multivariate mixed outcomes
Shonosuke Sugasawa, Jae Kwang Kim, Kosuke Morikawa
This paper proposes a flexible Bayesian approach to multiple imputation using conditional Gaussian mixtures. We introduce novel shrinkage priors for covariate-dependent mixing prop…
Semiparametric adaptive estimation under informative sampling
Kosuke Morikawa, Yoshikazu Terada, Jae Kwang Kim
In survey sampling, survey data do not necessarily represent the target population, and the samples are often biased. However, information on the survey weights aids in the elimina…
Identification enhanced generalised linear model estimation with nonignorable missing outcomes
Kenji Beppu, Jinung Choi, Kosuke Morikawa +1
Missing data often result in undesirable bias and loss of efficiency. These issues become substantial when the response mechanism is nonignorable, meaning that the response model d…