54 citations · 83 across the 13 of their papers we have counts for
15 papers
Efficient subsampling for high-dimensional data
Vasilis Chasiotis, Lin Wang, Dimitris Karlis
In the field of big data analytics, the search for efficient subdata selection methods that enable robust statistical inferences with minimal computational resources is of high imp…
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…
On the estimation of complex statistics combining different surveys
Vasilis Chasiotis, Dimitris Karlis
The importance of exploring a potential integration among surveys has been acknowledged in order to enhance effectiveness and minimize expenses. In this work, we employ the alignme…
Modelling handball outcomes using univariate and bivariate approaches
Dimitris Karlis, Rouven Michels, Marius Otting
Handball has received growing interest during the last years, including academic research for many different aspects of the sport. On the other hand modelling the outcome of the ga…
Extending the Dixon and Coles model: an application to women's football data
Rouven Michels, Marius Ötting, Dimitris Karlis
The prevalent model by Dixon and Coles (1997) extends the double Poisson model where two independent Poisson distributions model the number of goals scored by each team by moving p…
Optimal subdata selection for linear model selection
Vasilis Chasiotis, Dimitris Karlis
If the assumed model does not accurately capture the underlying structure of the data, a statistical method is likely to yield sub-optimal results, and so model selection is crucia…
On the selection of optimal subdata for big data regression based on leverage scores
Vasilis Chasiotis, Dimitris Karlis
The demand of computational resources for the modeling process increases as the scale of the datasets does, since traditional approaches for regression involve inverting huge data…
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…
Piecewise survival models: a change-point analysis on herpes zoster associated pain data revisited and extended
Dimitra Eleftheriou, Dimitris Karlis
For many diseases it is reasonable to assume that the hazard rate is not constant across time, but also that it changes in different time intervals. To capture this, we work here w…
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…
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…
A Bayesian approach for clustering skewed data using mixtures of multivariate normal-inverse Gaussian distributions
Yuan Fang, Dimitris Karlis, Sanjeena Subedi
Non-Gaussian mixture models are gaining increasing attention for mixture model-based clustering particularly when dealing with data that exhibit features such as skewness and heavy…
Clustering Discrete-Valued Time Series
Tyler Roick, Dimitris Karlis, Paul D. McNicholas
There is a need for the development of models that are able to account for discreteness in data, along with its time series properties and correlation. Our focus falls on INteger-v…
An integer-valued time series model for multivariate surveillance
Xanthi Pedeli, Dimitris Karlis
In recent days different types of surveillance data are becoming available for public health reasons. In most cases several variables are monitored and events of different types ar…
Model-based clustering using copulas with applications
Ioannis Kosmidis, Dimitris Karlis
The majority of model-based clustering techniques is based on multivariate Normal models and their variants. In this paper copulas are used for the construction of flexible familie…