79 citations · 99 across the 6 of their papers we have counts for
6 papers
Jeffreys-Type Penalized GEE for Correlated Binary Data with an Odds-Ratio Parameterization
Anestis Touloumis
Generalized estimating equations (GEE) are widely used for population-averaged inference on correlated binary responses, but ordinary GEE can fail under separation, a situation tha…
Bias-Reduced GEE via Adjusted Estimating Equations, with Odds-Ratio Extensions
Anestis Touloumis
Generalized estimating equations (GEE) are widely used for correlated data, but with small to moderate numbers of independent clusters the ordinary GEE regression estimators can be…
R Package multgee: A Generalized Estimating Equations Solver for Multinomial Responses
Anestis Touloumis
The R package multgee implements the local odds ratios generalized estimating equations (GEE) approach proposed by Touloumis et al. (2013), a GEE approach for correlated multinomia…
Nonparametric Stein-type Shrinkage Covariance Matrix Estimators in High-Dimensional Settings
Anestis Touloumis
Estimating a covariance matrix is an important task in applications where the number of variables is larger than the number of observations. Shrinkage approaches for estimating a h…
Hypothesis Testing for the Covariance Matrix in High-Dimensional Transposable Data with Kronecker Product Dependence Structure
Anestis Touloumis, John Marioni, Simon Tavaré
The matrix-variate normal distribution is a popular model for high-dimensional transposable data because it decomposes the dependence structure of the random matrix into the Kronec…
Testing the Mean Matrix in High-Dimensional Transposable Data
Anestis Touloumis, Simon Tavaré, John C. Marioni
The structural information in high-dimensional transposable data allows us to write the data recorded for each subject in a matrix such that both the rows and the columns correspon…