Y
Youngjo Lee
Researcher at Seoul National University
Publications - 246
Citations - 6438
Youngjo Lee is an academic researcher from Seoul National University. The author has contributed to research in topics: Random effects model & Generalized linear mixed model. The author has an hindex of 33, co-authored 234 publications receiving 5654 citations. Previous affiliations of Youngjo Lee include University of Cambridge & UPRRP College of Natural Sciences.
Papers
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Journal ArticleDOI
Hierarchical Generalized Linear Models
Youngjo Lee,John A. Nelder +1 more
TL;DR: In this article, a generalization of Henderson's joint likelihood, called a hierarchical or h-likelihood, for inferences from hierarchical generalized linear models is proposed, where the distribution of these components is not restricted to be normal; this allows a broader class of models, which includes generalized linear mixed models.
Book
Generalized Linear Models with Random Effects: Unified Analysis via H-likelihood
TL;DR: In this paper, the authors proposed an extended framework for estimating the likelihood of fixed parameters using a mixture of conditional and conditional likelihoods, which is derived from the profile likelihood distribution of the likelihood-ratio statistic distribution.
Journal ArticleDOI
Hierarchical generalised linear models: A synthesis of generalised linear models, random-effect models and structured dispersions
Youngjo Lee,John A. Nelder +1 more
TL;DR: In this article, a hierarchical generalised linear model (GLM) is developed as a synthesis of generalized linear models, mixed linear models and structured dispersions, and a restricted maximum likelihood method for the estimation of dispersion is extended to a wider class of models.
Journal ArticleDOI
Double hierarchical generalized linear models (with discussion)
Youngjo Lee,John A. Nelder +1 more
TL;DR: The h‐likelihood provides a unified framework for this new class of models and gives a single algorithm for fitting all members of the class, which will enable models with heavy‐tailed distributions to be explored and provide robust estimation against outliers.
Journal ArticleDOI
Conditional and Marginal Models: Another View
Youngjo Lee,John A. Nelder +1 more
TL;DR: It is shown that alleged differences in the behavior of parameters in so-called marginal and conditional models are based on a failure to compare like with like, and these seemingly apparent differences are meaningless because they are mainly caused by preimposed unidentifiable constraints on the random effects in models.