| Multivariate Poisson regression with covariance structure D Karlis, L Meligkotsidou Statistics and Computing 15, 255-265, 2005 | 191 | 2005 |
| Finite mixtures of multivariate Poisson distributions with application D Karlis, L Meligkotsidou Journal of statistical Planning and Inference 137 (6), 1942-1960, 2007 | 147 | 2007 |
| Quantile regression analysis of hedge fund strategies L Meligkotsidou, ID Vrontos, SD Vrontos Journal of Empirical Finance 16 (2), 264-279, 2009 | 113 | 2009 |
| Comparison of ISO-GUM and Monte Carlo methods for the evaluation of measurement uncertainty: Application to direct cadmium measurement in water by GFAAS D Theodorou, L Meligotsidou, S Karavoltsos, A Burnetas, M Dassenakis, ... Talanta 83 (5), 1568-1574, 2011 | 60 | 2011 |
| Detecting structural breaks and identifying risk factors in hedge fund returns: A Bayesian approach L Meligkotsidou, ID Vrontos Journal of Banking & Finance 32 (11), 2471-2481, 2008 | 51 | 2008 |
| A quantile regression approach to equity premium prediction L Meligkotsidou, E Panopoulou, ID Vrontos, SD Vrontos Journal of Forecasting 33 (7), 558-576, 2014 | 50 | 2014 |
| Exact filtering for partially observed continuous time models P Fearnhead, L Meligkotsidou Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2004 | 50 | 2004 |
| Forecasting with non-homogeneous hidden Markov models L Meligkotsidou, P Dellaportas Statistics and Computing 21, 439-449, 2011 | 47 | 2011 |
| Out-of-sample equity premium prediction: A complete subset quantile regression approach L Meligkotsidou, E Panopoulou, ID Vrontos, SD Vrontos The European Journal of Finance 27 (1-2), 110-135, 2021 | 28 | 2021 |
| Quantile forecast combinations in realised volatility prediction L Meligkotsidou, E Panopoulou, ID Vrontos, SD Vrontos Journal of the Operational Research Society 70 (10), 1720-1733, 2019 | 24 | 2019 |
| Bayesian multivariate Poisson mixtures with an unknown number of components L Meligkotsidou Statistics and Computing 17, 93-107, 2007 | 22 | 2007 |
| Augmentation schemes for particle MCMC P Fearnhead, L Meligkotsidou Statistics and Computing 26, 1293-1306, 2016 | 21 | 2016 |
| Longitudinal and time-to-drop-out joint models can lead to seriously biased estimates when the drop-out mechanism is at random C Thomadakis, L Meligkotsidou, N Pantazis, G Touloumi Biometrics 75 (1), 58-68, 2019 | 20 | 2019 |
| A Bayesian analysis of unit roots and structural breaks in the level, trend, and error variance of autoregressive models of economic series L Meligkotsidou, E Tzavalis, ID Vrontos Econometric Reviews 30 (2), 208-249, 2011 | 18 | 2011 |
| Maximum-likelihood estimation of coalescence times in genealogical trees L Meligkotsidou, P Fearnhead Genetics 171 (4), 2073-2084, 2005 | 17 | 2005 |
| Forecasting under model uncertainty: Non‐homogeneous hidden Markov models with Pòlya‐Gamma data augmentation C Koki, L Meligkotsidou, I Vrontos Journal of Forecasting 39 (4), 580-598, 2020 | 15 | 2020 |
| Filtering methods for mixture models P Fearnhead, L Meligkotsidou Journal of Computational and Graphical Statistics 16 (3), 586-607, 2007 | 14 | 2007 |
| Detecting structural breaks in multivariate financial time series: evidence from hedge fund investment strategies L Meligkotsidou, ID Vrontos Journal of Statistical Computation and Simulation 84 (5), 1115-1135, 2014 | 12 | 2014 |
| Postprocessing of genealogical trees L Meligkotsidou, P Fearnhead Genetics 177 (1), 347-358, 2007 | 9 | 2007 |
| On Bayesian analysis and unit root testing for autoregressive models in the presence of multiple structural breaks L Meligkotsidou, E Tzavalis, I Vrontos Econometrics and Statistics 4, 70-90, 2017 | 8 | 2017 |
