M
Michail Papathomas
Researcher at University of St Andrews
Publications - 27
Citations - 550
Michail Papathomas is an academic researcher from University of St Andrews. The author has contributed to research in topics: Categorical variable & Log-linear model. The author has an hindex of 8, co-authored 26 publications receiving 475 citations. Previous affiliations of Michail Papathomas include Athens University of Economics and Business & Imperial College London.
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Bayesian profile regression with an application to the National Survey of Children's Health.
TL;DR: This work proposes a method that addresses problems for categorical covariates by using, as its basic unit of inference, a profile formed from a sequence of covariate values, which is clustered into groups and associated via a regression model to a relevant outcome.
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PReMiuM: An R Package for Profile Regression Mixture Models Using Dirichlet Processes
TL;DR: PReMiuM as mentioned in this paper is a recently developed R package for Bayesian clustering using a Dirichlet process mixture model, which allows binary, categorical, count and continuous response, as well as continuous and discrete covariates.
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PReMiuM: An R Package for Profile Regression Mixture Models using Dirichlet Processes
TL;DR: PReMiuM is a recently developed R package for Bayesian clustering using a Dirichlet process mixture model, an alternative to regression models, non-parametrically linking a response vector to covariate data through cluster membership.
Journal ArticleDOI
Examining the joint effect of multiple risk factors using exposure risk profiles: lung cancer in nonsmokers.
TL;DR: It is concluded that profile regression is a powerful tool for identifying risk profiles that express the joint effect of etiologically relevant variables in multifactorial diseases.
Journal ArticleDOI
Exploring Data From Genetic Association Studies Using Bayesian Variable Selection and the Dirichlet Process: Application to Searching for Gene × Gene Patterns
Michail Papathomas,Michail Papathomas,John Molitor,John Molitor,Clive J. Hoggart,David I. Hastie,Sylvia Richardson,Sylvia Richardson +7 more
TL;DR: A new variable selection procedure that employs latent selection weights and is implemented in tandem with a Dirichlet process mixture model for the flexible clustering of genetic and epidemiological profiles is proposed.