24 citations · 91 across the 16 of their papers we have counts for
22 papers
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…
Analysis of mean-field approximation for Deffuant opinion dynamics on networks
Alina Dubovskaya, Susan C. Fennell, Kevin Burke +2
Mean-field equations have been developed recently to approximate the dynamics of the Deffuant model of opinion formation. These equations can describe both fully-mixed populations…
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…
Binomial confidence intervals for rare events: importance of defining margin of error relative to magnitude of proportion
Owen McGrath, Kevin Burke
Confidence interval performance is typically assessed in terms of two criteria: coverage probability and interval width (or margin of error). In this paper, we assess the performan…
An age-structured SEIR model for COVID--19 incidence in Dublin, Ireland with framework for evaluating health intervention cost
Fatima-Zahra Jaouimaa, Daniel Dempsey, Suzanne van Osch +4
Strategies adopted globally to mitigate the threat of COVID-19 have primarily involved lockdown measures with substantial economic and social costs with varying degrees of success.…
A Data-Driven Control-Theoretic Paradigm for Pandemic Mitigation with Application to Covid-19
Kevin Burke, B. Ross Barmish
In this paper, we introduce a new control-theoretic paradigm for mitigating the spread of a virus. To this end, our discrete-time controller, aims to reduce the number of new daily…
A generalised mean-field approximation for the Deffuant opinion dynamics model on networks
Susan C. Fennell, Kevin Burke, Michael Quayle +1
When the interactions of agents on a network are assumed to follow the Deffuant opinion dynamics model, the outcomes are known to depend on the structure of the underlying network.…
A Generalization of the Classical Kelly Betting Formula to the Case of Temporal Correlation
Joseph D. O'Brien, Kevin Burke, Mark E. Burke +1
For sequential betting games, Kelly's theory, aimed at maximization of the logarithmic growth of one's account value, involves optimization of the so-called betting fraction . I…
Agreement Threshold on Axelrod's model of Cultural Dissemination
Pádraig Mac Carron, Paul J. Maher, Susan Fennell +4
Shared opinions are an important feature in the formation of social groups. In this paper, we use the Axelrod model of cultural dissemination to represent opinion-based groups. In…
A modelling methodology for social interaction experiments
Susan C. Fennell, James P. Gleeson, Michael Quayle +2
Analysis of temporal network data arising from online interactive social experiments is not possible with standard statistical methods because the assumptions of these models, such…
An Alternative Formulation of Coxian Phase-type Distributions with Covariates: Application to Emergency Department Length of Stay
Jean Rizk, Kevin Burke, Cathal Walsh
In this paper we present a new methodology to model patient transitions and length of stay in the emergency department using a series of conditional Coxian phase-type distributions…
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…
Semiparametric multi-parameter regression survival modelling
Kevin Burke, Frank Eriksson, C. B. Pipper
We consider a log-linear model for survival data, where both the location and scale parameters depend on covariates and the baseline hazard function is completely unspecified. This…
Multi-Parameter Regression Survival Modelling: An Alternative to Proportional Hazards
Kevin Burke, Gilbert MacKenzie
It is standard practice for covariates to enter a parametric model through a single distributional parameter of interest, for example, the scale parameter in many standard survival…
A Flexible Parametric Modelling Framework for Survival Analysis
Kevin Burke, M. C. Jones, Angela Noufaily
We introduce a general, flexible, parametric survival modelling framework which encompasses key shapes of hazard function (constant, increasing, decreasing, up-then-down, down-then…
A Bivariate Power Generalized Weibull Distribution: a Flexible Parametric Model for Survival Analysis
M. C. Jones, Angela Noufaily, Kevin Burke
We are concerned with the flexible parametric analysis of bivariate survival data. Elsewhere, we have extolled the virtues of the "power generalized Weibull" (PGW) distribution as…
On the Non-uniqueness of Representations of Coxian Phase-Type Distributions
Jean Rizk, Kevin Burke, Cathal Walsh
Parameter estimation in Coxian phase-type models can be challenging due to their non-unique representation leading to a multi-modal likelihood. Since each representation correspond…
A likelihood-based approach for cure regression models
Kevin Burke, Valentin Patilea
We propose a new likelihood-based approach for estimation, inference and variable selection for parametric cure regression models in time-to-event analysis under random right-censo…
A Generalized Framework for Simultaneous Long-Short Feedback Trading
Joseph D. O'Brien, Mark E. Burke, Kevin Burke
We present a generalization of the Simultaneous Long-Short (SLS) trading strategy described in recent control literature wherein we allow for different parameters across the short…