17 citations · 26 across the 10 of their papers we have counts for
14 papers
Fraud Detection via Bayesian Positive-Unlabeled Learning with Gaussian Processes and Multilayer Networks
Konstantinos Bourazas, Angelos Alexopoulos, Petros Dellaportas +1
Tax fraud remains a central challenge for public revenue authorities worldwide, imposing fiscal losses estimated to reach up to 1 trillion euros annually in the EU alone. Fraudulen…
Bias-robust causal inference for panel data
Angelos Alexopoulos
We develop a bias-robust framework for causal inference in panel data models with interactive fixed effects. Conventional imputation-based estimators of average treatment effects o…
Exchangeable Gaussian Processes for Staggered-Adoption Policy Evaluation
Hayk Gevorgyan, Konstantinos Kalogeropoulos, Angelos Alexopoulos
We study the use of exchangeable multi-task Gaussian processes (GPs) for causal inference in panel data, applying the framework to two settings: one with a single treated unit subj…
On robust Bayesian causal inference
Angelos Alexopoulos, Nikolaos Demiris
This paper develops a Bayesian framework for robust causal inference from longitudinal observational data. Many contemporary methods rely on structural assumptions, such as factor…
Gaussian Invariant Markov Chain Monte Carlo
Michalis K. Titsias, Angelos Alexopoulos, Siran Liu +1
We develop sampling methods, which consist of Gaussian invariant versions of random walk Metropolis (RWM), Metropolis adjusted Langevin algorithm (MALA) and second order Hessian or…
A computationally efficient framework for realistic epidemic modelling through Gaussian Markov random fields
Angelos Alexopoulos, Paul Birrell, Daniela De Angelis
We tackle limitations of ordinary differential equation-driven Susceptible-Infections-Removed (SIR) models and their extensions that have recently be employed for epidemic nowcasti…
The heterogeneous causal effects of the EU's Cohesion Fund
Angelos Alexopoulos, Ilias Kostarakos, Christos Mylonakis +1
This paper estimates the causal effect of EU cohesion policy on regional output and investment, focusing on the Cohesion Fund (CF), a comparatively understudied instrument. Departi…
Real-time modelling of the SARS-CoV-2 pandemic in England 2020-2023: a challenging data integration
Paul J Birrell, Joshua Blake, Joel Kandiah +12
A central pillar of the UK's response to the SARS-CoV-2 pandemic was the provision of up-to-the moment nowcasts and short term projections to monitor current trends in transmission…
Variance Reduction for Metropolis-Hastings Samplers
Angelos Alexopoulos, Petros Dellaportas, Michalis K. Titsias
We introduce a general framework that constructs estimators with reduced variance for random walk Metropolis and Metropolis-adjusted Langevin algorithms. The resulting estimators r…
Evaluating the impact of local tracing partnerships on the performance of contact tracing for COVID-19 in England
Pantelis Samartsidis, Shaun R. Seaman, Abbie Harrison +7
Assessing the impact of an intervention using time-series observational data on multiple units and outcomes is a frequent problem in many fields of scientific research. In this pap…
A network and machine learning approach to detect Value Added Tax fraud
Angelos Alexopoulos, Petros Dellaportas, Stanley Gyoshev +3
Value Added Tax (VAT) fraud erodes public revenue and puts legitimate businesses at a disadvantaged position thereby impacting inequality. Identifying and combating VAT fraud befor…
Bayesian Variable Selection for Gaussian copula regression models
Angelos Alexopoulos, Leonardo Bottolo
We develop a novel Bayesian method to select important predictors in regression models with multiple responses of diverse types. A sparse Gaussian copula regression model is used t…
Bayesian prediction of jumps in large panels of time series data
Angelos Alexopoulos, Petros Dellaportas, Omiros Papaspiliopoulos
We take a new look at the problem of disentangling the volatility and jumps processes of daily stock returns. We first provide a computational framework for the univariate stochast…
Bayesian forecasting of mortality rates using latent Gaussian models
Angelos Alexopoulos, Petros Dellaportas, Jonathan J. Forster
We provide forecasts for mortality rates by using two different approaches. First we employ dynamic non-linear logistic models based on Heligman-Pollard formula. Second, we assume…