Spatial statistics, smoothing, Bayesian and computational statistics
Rana’s current research involves the application of geostatistical methods in spatial epidemiology and environmental monitoring. Along with researchers at Lancaster University, he developed an implementation of Markov chain Monte Carlo (MCMC) methodology to solve the inferential and prediction problems for model-based methods in geostatistics, leading to a Royal Statistical Society (RSS) read paper (1998). The continued extension of this seminal work resulted in joint publications with researchers from LSHTM, Institute of Terrestrial Ecology, Switzerland and Lancaster University. He has a record of publication in several other branches of statistics: non-parametric smoothing, longitudinal data analysis, functional data analysis, spatial point pattern analysis and application of Bayesian methods in quantile regression, the fitting of rating curves and progress test score estimation. He has vast experience of collaborating with non-statisticians, including medical researchers, astro-physicists and environmental scientists.
Rana is currently collaborating with the Peninsula Dental School on a joint project on Statistical Epidemiology in Oral Health. This involves analysis of dentistry data collected as part of the General Practice Patient Survey (GPPS).