Statistics
Te Tari Pāngarau me te Tatauranga
Department of Mathematics & Statistics

Upcoming seminars in Statistics

Seminars in Mathematics
Biostatistics in nutrition-related research

Dr Jill Haszard

Division of Sciences Biostatistician

Date: Thursday 18 April 2019
Time: 11:00 a.m.
Place: Room 241, 2nd floor, Science III building

Working as a biostatistician in the Department of Human Nutrition has exposed me to a wide variety of study designs and data. In particular, I handle a large amount of dietary data and am familiar with many of the statistical methods that are used to overcome the difficulties inherent when investigating dietary intake and nutritional status. As well as nutrition studies, I am also involved in studies exploring the influence of physical activity, sedentary behaviour, and sleep – all of which co-exist in a constrained space (the 24-hour day). This type of data requires compositional data analysis. However, using compositional data analysis needs careful interpretation of the statistical output. This is also an issue when analysing studies that assess associations with the gut microbiota.

190409093214
A tale of two paradigms: A case study of frequentist and Bayesian modelling for genetic linkage maps

Timothy Bilton

Department of Mathematics and Statistics

Date: Thursday 2 May 2019
Time: 11:00 a.m.
Place: Room 241, 2nd floor, Science III building

A genetic linkage map shows the relative position of and genetic distance between genetic markers, positions of the genome which exhibit variation, and underpins the study of species' genomes in a number of scientific applications. Genetic maps are constructed by tracking the transmission of genetic information from individuals to their offspring, which is frequently modelled using a hidden Markov model (HMM) since only the expression and not the transmission of genetic information is observed. Typically, HMMs for genetic maps are fitted using maximum likelihood. However, the uncertainty associated with genetic map estimates are rarely presented, and construction of confidence intervals using traditional frequentist methods are difficult, as many of the parameter estimates lie on the boundary of the parameter space. We investigate Bayesian approaches for fitting HMMs of genetic maps to facilitate characterizing uncertainty, and consider including a hierarchical component to improve estimation. Focus is given to constructing genetic maps using high-throughput sequencing data. Using simulated and real data, we compare the frequentist and Bayesian approaches and examine some of their properties. Lastly, the advantages/disadvantages of the two procedures and some issues encountered are discussed.

190412111057