Statistics Seminars  Autumn 2007

School of Mathematics, The University of Edinburgh

Friday 12th October 3.00 p.m. (EARLY START TIME 3PM)
Room 3218, JCMB    DR CHRISTINE HACKETT, BioSS SCRI
Linkage analysis in a mixed population of blackcurrant: some statistical 
detective work


Friday 2nd November 3.15 p.m. Room 5326, JCMB
TONY PETTIT, University of Lancaster
Statistical inference for assessing infection control measures for the 
transmission of pathogens in hospitals.


Friday 23rd November 3.15 p.m. Room 5326, JCMB
PETER HALL, University of Melbourne, (visiting University of Glasgow) 
Robustness of multiple hypothesis testing procedures against dependence.


Friday 7th December 3.15 p.m. Room 6206, JCMB
GUY NASON, University of Bristol
Costationarity and tests of stationarity for locally stationary time 
series with applications to econometrics

All seminars will take place in the James Clerk Maxwell Building at the 
King's Buildings site in Mayfield Road.  Tea and coffee will be 
available after the seminar in the Mathematics School, Staff Common Room 
(5212).  NOTE THAT CHRISTINE HACKETT’S SEMINAR STARTS AT 3.00 P.M.


Any enquiries about these Seminars should be made to

Colin Aitken, James Clerk Maxwell Building, Room 4605.
Phone: (0131) 650 4877
E-mail: [log in to unmask]

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ABSTRACTS

DR CHRISTINE HACKETT, BioSS SCRI
Linkage analysis in a mixed population of blackcurrant: some statistical 
detective work

The estimation of a linkage map of molecular markers is a prerequisite 
of studies to locate genes affecting important quantitative traits.  The 
estimation is straightforward if markers can be scored on a population 
derived from a cross between two inbred parents, but this is not 
possible in many plant species, especially bushy or tree species.  This 
talk focuses on the analysis of a mapping population in one such 
species, blackcurrant, and uses some exploratory statistics and simple 
genetic models to uncover some interesting features of the population.

PROFESSOR TONY PETTITT, University of Lancaster
Statistical inference for assessing infection control measures for the 
transmission of pathogens in hospitals.

Patients can acquire infections from pathogen sources within hospitals 
and certain pathogens appear to be found mainly in hospitals. 
Methicillin-resistant Staphylococcus Aureus (MRSA) is an example of a 
hospital acquired pathogen that continues to be of particular concern to 
patients and hospital management.  Patients infected with MRSA can 
develop severe infections which lead to increased patient morbidity and 
costs for the hospital.  Pathogen transmission to a patient occurs 
indirectly via health-care workers that do not regularly perform hand 
hygiene.  Infection control measures that can be considered include 
quarantine for colonised patients and improved hand hygiene for 
health-care workers.

The talk develops statistical methods and models in order to assess the 
effectiveness of the two control measures (i) isolation and (ii) 
improved hand hygiene.  For isolation, data from a prospective study 
carried out in a London hospital is considered and statistical models 
based on detailed patient data are used to determine the effectiveness 
of isolation.  The approach is Bayesian and involves Monte Carlo sampling.

For hand hygiene it is not possible, for ethical and practical reasons, 
to carry out a prospective study to investigate various levels of hand 
hygiene.  Instead hand hygiene effects are investigated by simulation 
using parameter values estimated from data on health-care worker hand 
hygiene and weekly colonisation incidence collected from a hospital ward 
in Brisbane.  Utilising a deterministic model for vector borne 
transmission of diseases, a Markov model is developed and used to 
estimate important transmission parameters.  Unfortunately for one 
transmission parameter there is little information available and an 
alternative approach based on the deterministic model eliminates this 
parameter so allowing the effects of changing hand hygiene to be 
investigated using simulation.

Conclusions about the effectiveness of the two infection control 
measures will be discussed and, from a modelling point of view, some 
conclusions will be made contrasting simulation models with statistical 
studies.

The talk involves collaborative work with Marie Forrester, Emma McBryde, 
Ben Cooper, Gavin Gibson and Sean McElwain.

PETER HALL, University of Melbourne (visiting University of Glasgow)
Robustness of multiple hypothesis testing procedures against dependence
Problems involving classification of high-dimensional data, and ‘highly 
multiple’ hypothesis testing, arise frequently in the analysis of 
genetic data and complex signals.  In this talk we show that, in the 
context of multiple hypothesis testing, the assumption of independence 
is much less of an issue in high-dimensional settings than in 
conventional, low-dimensional ones.  This is particularly true when the 
null distributions of test statistics are relatively light-tailed, for 
instance when they can plausibly be based on Normal approximations. 
These issues are related to the `upper tail independence' property, 
which is familiar in problems involving risk analysis.  Similar methods 
and ideas also lead to new insights for heavy-tailed data.

GUY NASON, University of Bristol
Costationarity and tests of stationarity for locally stationary time 
series with applications to econometrics.

Many real-world time series are often assumed to be stationary even when 
they are not.  Sometimes this has disastrous consequences.  This talk 
introduces some new tests for time series stationarity.  Given two time 
series it is often interesting to ask whether there is any association 
between them.  Various methods have been invented to ask this question 
(mostly for stationary series): cross-correlation, cross-spectral 
analysis and cointegration.  We introduce a new concept, called 
costationarity, which looks for linear combinations of locally 
stationary time series that are stationary.  If two time series are 
costationary then there exists a non-trivial, stochastic relationship 
that can be exploited.  We explain how our costationarity determination 
works and apply it to the FTSE100 and SP500 time series and show how the 
log-returns of these series are costationary.

-- 
Professor Colin Aitken,
Professor of Forensic Statistics,
School of Mathematics, King’s Buildings, University of Edinburgh,
Mayfield Road, Edinburgh, EH9 3JZ.

Tel:    0131 650 4877
E-mail:  [log in to unmask]
Fax :  0131 650 6553
http://www.maths.ed.ac.uk/~cgga