Probability and
Statistics Seminars: Winter 2006
Our seminars our usually held at 2.15 p.m. on Fridays
in room 3W
3.7 . If you wish to find out more, please
contact one of the organisers.
6/10/06: Julian Faraway
(Bath)
Statistics for Human Motion
Modeling
Statistical methods for the modeling of human motion
are presented. Various aspects of motion, decomposed
into univariate curves, 3D trajectories, shapes and
orientation trajectories are considered. Functional
data analytic methods for these data types are shown.
Applications to body motion modeling for design in
virtual manufacturing environments and facial motion
modeling in cleft palate patients are presented.
27/10/06: Ales Cerny (CASS)
On the Martingale Properties of
Good-Deal Price Bounds
The talk explores computation of so-called good-deal
price bounds and their relationship to certain
martingale measures and certain hedging problems. We
provide a unifying framework for the HARA class of
expected utility preferences which includes
quadratic, logarithmic and exponential utility
functions. We further explore links with indifference
pricing, q-optimal measures and f-divergences.
10/11/06: Sujit Sahu
(Southampton)
High Resolution Space-Time Ozone
Modeling for Assessing Trends
The assessment of air pollution regulatory programs
designed to improve ground level ozone concentrations
is a topic of considerable interest to environmental
managers. To aid this assessment, it is necessary to
model the space-time behavior of ozone for predicting
summaries of ozone across spatial domains of interest
and for the detection of long-term trends at
monitoring sites. These trends, adjusted for the
effects of meteorological variables, are needed for
determining the effectiveness of pollution control
programs in terms of their magnitude and
uncertainties across space. This paper proposes a
space-time model for daily 8-hour maximum ozone
levels to provide input to regulatory activities:
detection, evaluation, and analysis of spatial
patterns of ozone summaries and temporal trends. The
model is applied to analyzing data from the state of
Ohio which has been chosen because it contains a mix
of urban, suburban, and rural ozone monitoring sites
in several large cities separated by large rural
areas. The proposed space-time model is
auto-regressive and incorporates the most important
meteorological variables observed at a collection of
ozone monitoring sites as well as at several weather
stations where ozone levels have not been observed.
This problem of misalignment of ozone and
meteorological data is overcome by spatial modeling
of the latter. In so doing we adopt an approach based
on the successive daily increments in meteorological
variables. With regard to modeling, the increment (or
change-in-meteorology) process proves more attractive
than working directly with the meteorology process,
without sacrificing any desired inference. The full
model is specified within a Bayesian framework and is
fitted using MCMC techniques. Hence, full inference
with regard to model unknowns is available as well as
for predictions in time and space, evaluation of
annual summaries and assessment of trends.This is
joint work with Alan Gelfand and Dave Holland.
24/11/06: Oliver Johnson
(Bristol)
Maximum entropy and Poisson
approximation
I will show that the Poisson distribution maximises
entropy in the class of ultra log-concave
distributions (a class which includes sums of
Bernoulli variables). I will also explain how this
result relates to bounds in Poisson and compound
Poisson approximation.
8/12/06: Dankmar Boehning
(Reading)
Nonparametric estimation of
population size in closed capture-recapture
experiments in continuous time
The paper considers estimating the population size on
the basis of a continuous capture-recapture
experiment. As a result of this experiment counts are
observed indicating how often a unit has been
identified in the study. Zero counts remain
unobserved and need to be estimated. Chao (1987)
provided a lower bound for this frequency under the
assumption of a potential heterogeneous Poisson
process. We show here that there is an inequality
chain between all ratios of consecutive mixed Poisson
probabilities which can be utilized as a device for
the diagnosis of population heterogeneity. A further
generalization to a heterogeneous Power series
distribution is considered and an illustration from a
drug user monitoring study in Bangkok metropolis is
provided.
15/12/06: David Mason
(Delaware)
CANCELLED
There will be a
CAKE Seminar this week instead.
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