Estimating A Density of Microbial Densities
Department of Statistics
University of Waikato
Hamilton, New Zealand
Monday, 01 November 2004
I review the method of "Most Probable Number" for estimating the number
of micro-organisms in a physical sample using a dilution assay and show
how the problem may be formulated in Generalized Linear Model terms.
Then I look at the harder problem of estimating the parameters of a
log-normal distribution of microbial densities in a region given the
results of dilution assays at a random sample of sites in the region. My
approach uses the EM algorithm but could be seen as a generalized linear mixed model.
I apply the method to a large set of 725 individual dilution assays for
Campylobacter in New Zealand freshwater environments.
We will discuss some applications of this result, including its connection to K means clustering. Time permits, we will also discuss some relatedwork in mathematical finance, including financial time series data analysis (with Chan of CSIRO) and credit risk evaluations (with Jarrow and Zeng of Cornell U.).
Refreshments will be served at 3:45 p.m. in Room 008 of the Statistics Building