2 citations · 6 across the 8 of their papers we have counts for
11 papers · 1 filter
Robust Distributional Regression with Automatic Variable Selection
Meadhbh O'Neill, Kevin Burke
Datasets with extreme observations and/or heavy-tailed error distributions are commonly encountered and should be analyzed with careful consideration of these features from a stati…
A Statistical-Modelling Approach to Feedforward Neural Network Model Selection
Andrew McInerney, Kevin Burke
Feedforward neural networks (FNNs) can be viewed as non-linear regression models, where covariates enter the model through a combination of weighted summations and non-linear funct…
Multi-Parameter Regression Survival Modelling with Random Effects
Fatima-Zahra Jaouimaa, Il Do Ha, Kevin Burke
We consider a parametric modelling approach for survival data where covariates are allowed to enter the model through multiple distributional parameters, i.e., scale and shape. Thi…
Variable Selection Using a Smooth Information Criterion for Distributional Regression Models
Meadhbh O'Neill, Kevin Burke
Modern variable selection procedures make use of penalization methods to execute simultaneous model selection and estimation. A popular method is the LASSO (least absolute shrinkag…
Penalized Variable Selection in Multi-Parameter Regression Survival Modelling
Fatima-Zahra Jaouimaa, Il Do Ha, Kevin Burke
Multi-parameter regression (MPR) modelling refers to the approach whereby covariates are allowed to enter the model through multiple distributional parameters simultaneously. This…
A Multi-parameter regression model for interval censored survival data
Defen Peng, Gilbert MacKenzie, Kevin Burke
We develop flexible multi-parameter regression survival models for interval censored survival data arising in longitudinal prospective studies and longitudinal randomised controlle…