Experts directory

Prof E Ranganai

College of Science, Engineering and Technology
School of Science
Department: Statistics
Associate Professor
Tel: 011 670 9257


  • Applied Statistics
  • Distribution theory
  • Energy (including renewable) forecasting
  • Quantile Regression
  • Regression Diagnostics
  • Time Series Analysis


  • PhD in Statistics (Stellenbosch, South Africa) 

Fields of academic interests

  • Theory and Applications of Quantile Regression
  • Regression and Regression Diagnostics
    • On the Conditional Mean function and the whole range of Quantile Regression Functions.
    • Quantile Regression Modelling under covariate measurement error such as prevalent in medical data.
  • Applications of Quantile Regression and Time Series Analysis in Modelling:
    • Renewable Energy and Energy Load Forecasting and
    • Precious Metals.

Field of Specialisation

  • Quantile Regression
  • Time Series Analysis

Journal articles

  1. E. Ranganai & S. Nadarajah, A predictive leverage statistic for quantile regression with measurement errors. Communication in Statistics- Simulation and Computation, DOI: 10.1080/03610918.2016.1204455, 2017: ISSN: 0361-0918 (Print) 1532-4141.
  2. E. Ranganai, Quality of fit measurement in regression quantiles: An elemental set method approach. Statistics & Probability Letters, 2016: 111, 18–25. ISSN: 0167-7152.
  3. E. Ranganai, On studentized residuals in the quantile regression framework. SpringerPlus, 5(1); DOI: 10.1186/s40064-016-2898-6, 2016: 5, 1-11. ISSN: 2193-1801.
  4. E. Ranganai & S. B. Kubheka Long Memory Mean and Volatility Models of Platinum and Palladium Price Return Series under Heavy Tailed Distributions. SpringerPlus, 5:2089; DOI: 10.1186/s40064-016-3768-y, 2016: 5, 1-20. ISSN: 2193-1801.
  5. E. Ranganai & M. B. Nzuza, A comparative Study of the Seasonal Autoregressive Integrated Moving Average (SARIMA) Models and Harmonically Coupled SARIMA Models in the Analysis and Forecasting of Seasonal Solar Radiation Data: A case study in Durban, South Africa. Journal of Energy in Southern Africa, 2015: 26, 125-137. ISSN 1021-447X online.
  6. E. Ranganai, J. O. van Vuuren & T. de Wet Multiple Case High Leverage Diagnosis in Regression Quantiles. Communications in Statistics-Theory and Methods, DOI: 0.1080/03610926.2012.715225, 2014: 43, 3343–3370. ISSN: 0361-0926.

Professional positions, fellowships & awards

  • South African Statistical Association (SASA)
  • International Biometric Society (IBS)


  • Quantile Regression Diagnostics via Elemental Regression
  • Theory and Applications of Quantile Regression
    • Incidence of stroke under covariate measurement error.
  • Long Memory Time Series including GARCH and FIGARCH TYPE Models, Extremal Quantile Regression & Var Models.
    • Applications in renewable energy, precious metals.
    • Peak Electricity Demand Forecasting
  • Characterization of renewable energy in Africa using Quantile Regression