Jennifer Brown

ProfessorJennifer Brown

Jack Erskine 622
Internal Phone: 95892

Qualifications & Memberships

Research Interests

My primary research interest is in environmental statistics and I have expertise in survey design and environmental monitoring. Research in environmental statistics is collaborative by its very nature, and I work with both statisticians and biologists. As an applied statistician I am involved in projects in a variety of application areas and as such publish in a wide range of journals.

I have an international reputation in survey design and environmental monitoring and am often asked by overseas agencies for advice and assistance in designing environmental monitoring programmes, specifically designs for animal and plant monitoring. I have provided advice and support in France, Italy, Spain, USA and Australia, as well as in New Zealand. I have hosted international workshops on survey design and environmental monitoring.

My research interests are broad and go beyond environmental statistics. I work in other application areas, most recently, human health and wellbeing. I also work in theoretical statistics, with publications in topics ranging from theoretical sampling to regression trees.

I am committed to developing leadership in the mathematical sciences, including statistics and data science. I provide advice and support for emerging leaders.

Recent Publications

  • Haq A., Bibi N., Khoo MBC. and Brown J. (2022) Monitoring the process coefficient of variation without subgrouping. Journal of Statistical Computation and Simulation 92(9): 1805-18022. http://dx.doi.org/10.1080/00949655.2021.2007918.
  • Robertson B., Ozturk O., Kravchuk O. and Brown J. (2022) Spatially Balanced Sampling with Local Ranking. Journal of Agricultural, Biological, and Environmental Statistics http://dx.doi.org/10.1007/s13253-022-00501-6.
  • Haq A., Bibi M. and Brown J. (2021) Monitoring multivariate simple linear profiles using individual observations. Journal of Statistical Computation and Simulation 91(17): 3573-3592. http://dx.doi.org/10.1080/00949655.2021.1943665.
  • Haq A., Khoo M. and Brown J. (2021) Memory-type t charts with multiple auxiliary information for the process mean. Quality and Reliability Engineering International http://dx.doi.org/10.1002/qre.2946.
  • Ozturk O., Kravchuk O. and Brown J. (2021) Two-stage cluster samples with judgment post-stratification. Canadian Journal of Statistics http://dx.doi.org/10.1002/cjs.11644.