33 citations · 46 across the 13 of their papers we have counts for
13 papers
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…
Robust propensity score weighting estimation under missing at random
Hengfang Wang, Jae Kwang Kim, Jeongseop Han +1
Missing data is frequently encountered in many areas of statistics. Propensity score weighting is a popular method for handling missing data. The propensity score method employs a…
Maximum Likelihood Imputation
Jeongseop Han, Youngjo Lee, Jae Kwang Kim
Maximum likelihood (ML) estimation is widely used in statistics. The h-likelihood has been proposed as an extension of Fisher's likelihood to statistical models including unobserve…
Enhanced Laplace Approximation
Jeongseop Han, Youngjo Lee
The Laplace approximation (LA) has been proposed as a method for approximating the marginal likelihood of statistical models with latent variables. However, the approximate maximum…
On rereading Savage
Yudi Pawitan, Youngjo Lee
If we accept Savage's set of axioms, then all uncertainties must be treated like ordinary probability. Savage espoused subjective probability, allowing, for example, the probabilit…
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…
Popper's falsification and corroboration from the statistical perspectives
Youngjo Lee, Yudi Pawitan
The role of probability appears unchallenged as the key measure of uncertainty, used among other things for practical induction in the empirical sciences. Yet, Popper was emphatic…
Resolving the induction problem: Can we state with complete confidence via induction that the sun rises forever?
Youngjo Lee
Induction is a form of reasoning that starts with a particular example and generalizes to a rule, namely, a hypothesis. However, establishing the truth of a hypothesis is problemat…
Robust functional regression model for marginal mean and subject-specific inferences
Chunzheng Cao, Jian Qing Shi, Youngjo Lee
We introduce flexible robust functional regression models, using various heavy-tailed processes, including a Student -process. We propose efficient algorithms in estimating para…
Extended T-process Regression Models
Zhanfeng Wang, Jianqing Shi, Youngjo Lee
Gaussian process regression (GPR) model has been widely used to fit data when the regression function is unknown and its nice properties have been well established. In this article…
Automatic Detection of Significant Areas for Functional Data with Directional Error Control
Peirong Xu, Youngjo Lee, Jian Qing Shi
To detect differences between the mean curves of two samples in longitudinal study or functional data analysis, we usually need to partition the temporal or spatial domain into sev…
Likelihood Inference for Models with Unobservables: Another View
Youngjo Lee, John A. Nelder
There have been controversies among statisticians on (i) what to model and (ii) how to make inferences from models with unobservables. One such controversy concerns the difference…
Rejoinder: Likelihood Inference for Models with Unobservables Another View
Youngjo Lee, John A. Nelder
Rejoinder to "Likelihood Inference for Models with Unobservables: Another View" by Youngjo Lee and John A. Nelder [arXiv:1010.0303]