12 citations · 13 across the 4 of their papers we have counts for
4 papers
Statistical Inference with Different Missing-data Mechanisms
Kosuke Morikawa, Yutaka Kano
When data are missing due to at most one cause from some time to next time, we can make sampling distribution inferences about the parameter of the data by modeling the missing-dat…
Identification Problem for The Analysis of Binary Data with Non-ignorable Missing
Kosuke Morikawa, Yutaka Kano
When a missing-data mechanism is NMAR or non-ignorable, missingness is itself vital information and it must be taken into the likelihood, which, however, needs to introduce additio…
Full information maximum likelihood estimation in factor analysis with a lot of missing values
Kei Hirose, Sunyong Kim, Yutaka Kano +3
We consider the problem of full information maximum likelihood (FIML) estimation in a factor analysis model when a majority of the data values are missing. The expectation-maximiza…
Discovery of non-gaussian linear causal models using ICA
Shohei Shimizu, Aapo Hyvarinen, Yutaka Kano +1
In recent years, several methods have been proposed for the discovery of causal structure from non-experimental data (Spirtes et al. 2000; Pearl 2000). Such methods make various as…