Applications of statistics to the population dynamics and distribution of birds, reptiles, amphibians, mammals, fish, insects and plants (R. Altwegg, A. E. Clark, G. Distiller, I. Durbach, B. Erni, M. Varughese, V. Visser). The centre for Statistics in Ecology, Environment and Conservation (SEEC) is a research group within the department that focuses, among other things, on demography as an important tool for understanding population ecology.
Applications of statistics to problems in genetics (M. Lacerda).
Medical applications of statistics ( F. Gumedze, M. Lacerda, F. Little, SP Silal, R Kassanjee, W. Msemburi). The objectives of the Biostatistics Interest group are to develop statistical methodology motivated by medical problems and to provide statistical support to medical researchers.
Data science uses computer-intensive statistical methods to identify patterns and make predictions using large volumes of data. Specific interests include statistical and machine learning techniques. (I. Durbach, S. Er, M. Lacerda, F. Little, J. Nyirenda, M. Varughese, N. Watson, T. Gebbie, M.Z. Ngwenya, E.A.D. Pienaar, S.S Britz, W. Msemburi).
The development of interactive decision aids, to assist in the analysis of decision problems with multiple and conflicting objectives, with particular reference to natural resource management and others; Applications of Bayesian statistical analysis for complex decision making contexts; Bayesian networks as a modelling tool to support decision making; Links between scenario planning and decision analysis (I. Durbach, T.J. Stewart).
Application of statistics to conservation decision making, climate change, understanding of biodiversity patterns, evolutionary ecology and macroecology(R. Altwegg, A.E. Clark, G. Distiller, I. Durbach, B. Erni, L Haines, M. Varughese, V. Visser, M.Z. Ngwenya).
Econometric techniques are being used to test theories related to the South African economy in the fields of finance, monetary economics, interest rate theory and stock market research (G.D.I. Barr, T. Gebbie).
Data analysis and data-science of high and low frequency financial market market data. Calibration of models of financial market interactions. Top-down and bottom-up causation, and the relationships between various agents operating in financial markets. Unsupervised learning and cluster analysis. Data driven financial market simulation. (T. Gebbie)
Applied Financial Research into macro-economic and company-specific determinants of Johannesburg Stock Exchange share behaviour. Quantitative Analysis and Risk Management of share portfolios.(G.D.I. Barr, D.J. Bradfield, CK Huang, A.E. Clark, T. Gebbie, E.A.D. Pienaar).
Application of mathematical modelling and computer simulation to predict the dynamics and control of infectious diseases to evaluate the potential impact of control programmes in reducing morbidity and mortality. (SP Silal, R Kassanjee, W. Msemburi)
Longitudinal data analysis, analysis of repeated measures data, generalised linear and non-linear mixed effect models, models for time-to-event data, joint modelling and causal modelling. (B. Erni, F. Gumedze, F. Little, C. Thiart, R Kassanjee, W. Msemburi).
Data analysis, control and modeling of the limit-order book for trading and investing. Statistical learning, online learning and Machine Learning applied to financial market data. (T. Gebbie)
Multivariate distribution theory; multidimensional scaling, correspondence analysis and cluster analysis, robust regression procedures, classification and discrimination procedures, graphical displays of multivariate data, structural equation modelling. (S. Er, C. Thiart, T. Gebbie, M.Z. Ngwenya).
Inference, analysis and simulation for time inhomogeneous stochastic processes with non-linear dynamics, and first passage time problems for time-inhomogeneous processes (E.A.D. Pienaar).
Application of OR models for decision making and planning in private and public sectors; combinatorial optimization; (I. Durbach, J. Nyirenda, R. Rakotonirainy, L. Scott, SP Silal, T.J. Stewart, N Watson).
The design of experiments in agriculture, biology, industry and medicine which are in some sense optimal (L.M. Haines).
Statistics of large geoscience datasets, Geographic information systems (GIS), Geostatistics and spatial modelling, Mixed models with spatial data (B. Erni, L.M. Haines, C. Thiart, M. Varughese, M.Z. Ngwenya, W. Msemburi).
Research into VBA/Excel simulation and spreadsheet-based methods for teaching Statistics, as well as the use of technological aides in the classroom and for online learner support and teaching. (G.D.I. Barr, L. Scott, S.S. Britz, W. Msemburi)