1 citations · 2 across the 5 of their papers we have counts for
5 papers
Subject-specific Deep Neural Networks for Count Data with High-cardinality Categorical Features
Hangbin Lee, Il Do Ha, Changha Hwang +1
There is a growing interest in subject-specific predictions using deep neural networks (DNNs) because real-world data often exhibit correlations, which has been typically overlooke…
Point Mass in the Confidence Distribution: Is it a Drawback or an Advantage?
Hangbin Lee, Youngjo Lee
Stein's (1959) problem highlights the phenomenon called the probability dilution in high dimensional cases, which is known as a fundamental deficiency in probabilistic inference. T…
Statistical Inference for Random Unknowns via Modifications of Extended Likelihood
Hangbin Lee, Youngjo Lee
Fisher's likelihood is widely used for statistical inference for fixed unknowns. This paper aims to extend two important likelihood-based methods, namely the maximum likelihood pro…
Deep Neural Networks for Semiparametric Frailty Models via H-likelihood
Hangbin Lee, IL DO HA, Youngjo Lee
For prediction of clustered time-to-event data, we propose a new deep neural network based gamma frailty model (DNN-FM). An advantage of the proposed model is that the joint maximi…
Epistemic confidence, the Dutch Book and relevant subsets
Yudi Pawitan, Hangbin Lee, Youngjo Lee
We use a logical device called the Dutch Book to establish epistemic confidence, defined as the sense of confidence \emph{in an observed} confidence interval. This epistemic proper…