Academic Qualifications:
B.Sc. (Risk Analysis), 2002, University of the Free State
M.Sc. (Risk Analysis), 2004 (with distinction), University of the Free State
Ph.D. (Mathematical Statistics), 2008, University of the Free State
Employment History:
October 2005 – June 2011: Lecturer in the Department of Mathematical Statistics and Actuarial Science at the University of the Free State.
July 2011 – Current: Senior lecturer in the Department of Mathematical Statistics and Actuarial Science at the University of the Free State.
Awards:
Y2-rating by the National Research Foundation of South Africa
Reived funding from the National Research Foundation’s (NRF) Thuthuka Programme for my research project, High quantile prediction and tail estimation in 2013.
Selected for the Vice-Chancellor’s Prestige Scholars Programme as one of the most promising young scientists at the University of the Free State in 2011.
Membership:
Member of the SASA (South Africa Statistical Association).
Committees:
Co-editor of SASA proceedings
Chair of the Extreme Value chapter of SASA
NRF Statistics panel
De Waal, D.J., Van Gelder, P.H.A.J.M. and Nel, A. 2007. Estimating joint tail probabilities of river discharges through the Logistic Copula. Environmetrics. 18, 621—631.
Verster, A and De Waal, D.J. 2010. Investigating approximations and parameter estimation of the multivariate Generalized Burr-Gamma. South African Statistical Journal. 44, 159 – 192.
Verster, A and De Waal, D.J. 2011. A method for choosing an optimum threshold if the underlying distribution is generalized Burr-Gamma. South African Statistical Journal. 45:2, 273 – 292.
Verster, A, D.J. de Waal, R. Schall and C. Prins. 2012. A truncated Pareto model to estimate the under recovery of large diamonds. Journal of Mathematical Geosciences. 44, 91 – 100.
De Waal, D.J and Verster, A. 2012. Modelling high river flows and Southern Oscillation Index jointly. Journal for Structure and Infrastructure Engineering. 8, 367:372.
Chikobvu, D, Siguake, C and Verster, A. 2012. Winter peak electricity load forecasting in South Africa using extreme value theory. South African Statistical Journal. 46:2, 377 – 393.
Verster, A, Chikobvu, D and Siguake, C. 2012. Analysis of the same day of the week increases in peak electricity demand in South Africa. ORION. 29, 125 – 136.
Siguake, C, Verster, A and Chikobvu, D. 2013. Extreme daily increases in peak electricity demand: tail quantile estimation. Energy Policy. 53, 90 – 96.
Verster, A and De Waal, D.J. 2013. The Generalized t-distribution, a generalization of the positive tail of the t distribution. South African Statistical Journal. 47:1, 71 – 82.
Goegebeur, Y, Guillou, A and Verster, A. 2014. Robust and asymptotically unbiased estimation of extreme quantiles for heavy tailed distibutions. Statistical Probability Letters. 87, 108 – 114.
Beirlant, J, Maribe, G and Verster, A. 2017. Using shrinkage estimators to reduce bias and MSE and estimation of heavy tails. To appear in REVSTAT. 201
Beirlant J, Maribe G, Verster A. (2019). Using shrinkage estimators to reduce bias and MSE in estimation of heavy tails. REVSTAT Statistical Journal, 17, 91 – 108.
Beirlant J, Maribe G, Naveau P, Verster A. (2022). Bias Reduced Peak over Threshold Tail Estimation. REVSTAT Statistical Journal, 20, 277 – 304.
Verster A, Raubenheimer L. (2020). A Different Approach for Choosing a Threshold in Peak over Threshold. Statistics Optimization and Information Computing, 9, 838 - 848.
Verster A, Kwaramba N. (2022). A Different Way of Choosing a Threshold in a Bivariate Extreme Value Study. Statistics Optimization and Information Computing, 10, 505 – 518.
Verster A, Kwaramba N. (2022). Estimating the dependence parameter in bivariate extreme value statistics through a Bayesian approach. ORiON, 38 (2), 107 – 121.