599 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- National and Kapodistrian University of AthensGR24 papers
- National Technical University of AthensGR11 papers
- Athena Research and Innovation Center In Information Communication & Knowledge TechnologiesGR9 papers
- Athens State UniversityUS7 papers
- Centre National de la Recherche ScientifiqueFR7 papers
- Centrum Wiskunde & InformaticaNL7 papers
- Delft University of TechnologyNL7 papers
- École PolytechniqueFR7 papers
- University College LondonGB7 papers
- University of OxfordGB7 papers
- Laboratoire d'Informatique de l'École PolytechniqueFR6 papers
- University of AmsterdamNL6 papers
27 papers · 1 filter
Bayesian Cluster Weighted Gaussian Models
Panagiotis Papastamoulis, Konstantinos Perrakis
We introduce a novel class of Bayesian mixtures for normal linear regression models which incorporates a further Gaussian random component for the distribution of the predictor var…
Directional data analysis using the spherical Cauchy and the Poisson kernel-based distribution
Michail Tsagris, Panagiotis Papastamoulis, Shogo Kato
In 2020, two novel distributions for the analysis of directional data were introduced: the spherical Cauchy distribution and the Poisson kernel-based distribution. This paper provi…
A Model-Based Approach to Shot Charts Estimation in Basketball
Luca Scrucca, Dimitris Karlis
Shot charts in basketball analytics provide an indispensable tool for evaluating players' shooting performance by visually representing the distribution of field goal attempts acro…
Multinomial mixture for spatial data
Anna Nalpantidi, Dimitris Karlis, Panagiotis Papastamoulis
The purpose of this paper is to extend standard finite mixture models in the context of multinomial mixtures for spatial data, in order to cluster geographical units according to d…
Bayesian inference and cure rate modeling for event history data
Panagiotis Papastamoulis, Fotios Milienos
Estimating model parameters of a general family of cure models is always a challenging task mainly due to flatness and multimodality of the likelihood function. In this work, we pr…
Subdata selection for big data regression: an improved approach
Vasilis Chasiotis, Dimitris Karlis
In the big data era researchers face a series of problems. Even standard approaches/methodologies, like linear regression, can be difficult or problematic with huge volumes of data…
Sample size determination via learning-type curves
Alimu Dayimu, Nikola Simidjievski, Nikolaos Demiris +1
This paper is concerned with sample size determination methodology for prediction models. We propose combining the individual calculations via a learning-type curve. We suggest two…
Inconsistency identification in network meta-analysis via stochastic search variable selection
Georgios Seitidis, Stavros Nikolakopoulos, Ioannis Ntzoufras +1
The reliability of the results of network meta-analysis (NMA) lies in the plausibility of key assumption of transitivity. This assumption implies that the effect modifiers' distrib…
Model based clustering of multinomial count data
Panagiotis Papastamoulis
We consider the problem of inferring an unknown number of clusters in replicated multinomial data. Under a model based clustering point of view, this task can be treated by estimat…
Misuse of the sign test in narrative synthesis of evidence
Stavros Nikolakopoulos
In narrative synthesis of evidence, it can be the case that the only quantitative measures available concerning the efficacy of an intervention is the direction of the effect, i.e.…
A Metropolized adaptive subspace algorithm for high-dimensional Bayesian variable selection
Christian Staerk, Maria Kateri, Ioannis Ntzoufras
A simple and efficient adaptive Markov Chain Monte Carlo (MCMC) method, called the Metropolized Adaptive Subspace (MAdaSub) algorithm, is proposed for sampling from high-dimensiona…
Extending the Mann-Kendall test to allow for measurement uncertainty
Stavros Nikolakopoulos, Eric Cator, Mart P. Janssen
The Mann-Kendall test for trend has gained a lot of attention in a range of disciplines, especially in the environmental sciences. One of the drawbacks of the Mann-Kendall test whe…
Bayesian Evidence Synthesis for the common effect model
Stavros Nikolakopoulos, Björn Alfons Edmar, Ioannis Ntzoufras
Bayes Factors, the Bayesian tool for hypothesis testing, are receiving increasing attention in the literature. Compared to their frequentist rivals (-values or test statistics),…
Bayesian inference for transportation origin-destination matrices: the Poisson-inverse Gaussian and other Poisson mixtures
Konstantinos Perrakis, Dimitris Karlis, Mario Cools +1
In this paper we present Poisson mixture approaches for origin-destination (OD) modeling in transportation analysis. We introduce covariate-based models which incorporate different…
Modelling of functional profiles and explainable shape shifts detection: An approach combining the notion of the Fréchet mean with the shape invariant model
Georgios I. Papayiannis, Stelios Psarakis, Athanasios N. Yannacopoulos
A modelling framework suitable for detecting shape shifts in functional profiles combining the notion of Fréchet mean and the concept of deformation models is developed and propose…
Infinite mixtures of multivariate normal-inverse Gaussian distributions for clustering of skewed data
Yuan Fang, Dimitris Karlis, Sanjeena Subedi
Mixtures of multivariate normal inverse Gaussian (MNIG) distributions can be used to cluster data that exhibit features such as skewness and heavy tails. However, for cluster analy…
On the identifiability of Bayesian factor analytic models
Panagiotis Papastamoulis, Ioannis Ntzoufras
A well known identifiability issue in factor analytic models is the invariance with respect to orthogonal transformations. This problem burdens the inference under a Bayesian setup…
Power-Expected-Posterior Priors as Mixtures of g-Priors
Dimitris Fouskakis, Ioannis Ntzoufras
One of the main approaches used to construct prior distributions for objective Bayes methods is the concept of random imaginary observations. Under this setup, the expected-posteri…
Clustering Multivariate Data using Factor Analytic Bayesian Mixtures with an Unknown Number of Components
Panagiotis Papastamoulis
Recent work on overfitting Bayesian mixtures of distributions offers a powerful framework for clustering multivariate data using a latent Gaussian model which resembles the factor…
Probability Based Independence Sampler for Bayesian Quantitative Learning in Graphical Log-Linear Marginal Models
Ioannis Ntzoufras, Claudia Tarantola, Monia Lupparelli
Bayesian methods for graphical log-linear marginal models have not been developed in the same extent as traditional frequentist approaches. In this work, we introduce a novel Bayes…
Pairwise likelihood estimation of latent autoregressive count models
Xanthi Pedeli, Cristiano Varin
Latent autoregressive models are useful time series models for the analysis of infectious disease data. Evaluation of the likelihood function of latent autoregressive models is int…
Forecasting under model uncertainty:Non-homogeneous hidden Markov models with Polya-Gamma data augmentation
Constandina Koki, Loukia Meligkotsidou, Ioannis Vrontos
We consider two-state Non-Homogeneous Hidden Markov Models (NHHMMs) for forecasting univariate time series. Given a set of predictors, the time series are modeled via predictive re…
Variations of Power-Expected-Posterior Priors in Normal Regression Models
Dimitris Fouskakis, Ioannis Ntzoufras, Konstantinos Perrakis
The power-expected-posterior (PEP) prior is an objective prior for Gaussian linear models, which leads to consistent model selection inference, under the M-closed scenario, and ten…
The Hamming Ball Sampler
Michalis K. Titsias, Christopher Yau
We introduce the Hamming Ball Sampler, a novel Markov Chain Monte Carlo algorithm, for efficient inference in statistical models involving high-dimensional discrete state spaces. T…