Department of Statistics
Colloquium
Spring 2008
All the seminars are held in the meeting room of the
Department of Statistics (
Please contact Hongmei Jiang at hongmei@northwestern.edu if there is a speaker you would like to be invited to speak in this seminar.
Please send an e-mail to hongmei@northwestern.edu if you want to be added to the e-mail list of the seminar announcement.
April 9, Wednesday, 12pm
Professor Heping Zhang, Biostatistics, Director of Collaborative Center for Statistics in Science,
Title: Joint Modeling of Time Series Measures and Recurrent Events and Analysis of the Effects of Air Quality on Respiratory Symptoms
Abstract: Exposure to ambient pollutants at concentrations above defined standards is a risk factor for respiratory symptoms, especially in sensitive children. Many studies have been undertaken to monitor air quality and to assess its association with respiratory symptoms. We propose a joint mixed effects regression model of time series measures and recurrent events to analyze the air quality and respiratory symptom data from the Yale Mothers and Infants Health Study.
Three mothers' symptoms (runny nose, cough, and sore throat) and three infants' symptoms (runny nose, cough, and general sickness) were investigated. To alleviate the computational complexity, a two-stage maximum likelihood based estimation procedure is introduced to estimate the parameters, and simulation studies are conducted to assess the validity of this estimation procedure.
Our analysis reveals differences in the etiology of respiratory symptoms between mothers and infants. Most notably, coarse particles of mass between 2.5 and 10 microns in diameter increased the risks of mothers' runny nose and cough symptoms, but had no significant impact on any of the three infants' symptoms. The sulfate level was negatively associated with the risk of infants' runny nose and cough symptoms, but had no significant effects on any of the three mothers' symptoms. High level of humidity is negatively associated with the mothers' cough incidence, but had no significant association on any of the three infants' symptoms. Such differences reveal not only the sensitivity of the mothers and infants to the air quality, but also call for further understanding of the differences. It is possible that actions taken to overcome humidity by mothers may inadvertently affect the infants.
This is a joint work with Yuanqing Ye, Peter Diggle, and Jian Shi.
April 23, Wednesday, 12pm
Professor Dan
Nordman, Department of Statistics,
Title: Tapered empirical likelihood for time series data
Abstract: This
talk aims to motivate and describe a formulation of empirical likelihood for
time series inference based on tapered data blocks. Data blocks are a
device for capturing the time dependence and the proposed method involves
tapering these blocks in a special way. The resulting empirical likelihood has
chi squared limits for nonparametrically calibrating
confidence intervals for time series parameters, such as means and
correlations. Tapering is shown to improve the chi-squared approximation
and enhance the coverage accuracy of intervals compared to untapered
empirical likelihood versions. Simulation evidence is provided and block
choices are considered as well.
May 7, Wednesday, 12pm
Professor
Ginger Davis, Department of Systems and Information Engineering,
Title: Hierarchical Bayesian Markov Switching Models with Application to Predicting Spawning Success of Shovelnose Sturgeon
Abstract: The
timing of spawning in fish is tightly linked to environmental factors however
these factors are not very well understood for many species. Specifically,
little information is available to guide recruitment efforts for endangered
species such as the sturgeon. Therefore, we propose a Bayesian hierarchical
model for predicting spawning success of the shovelnose sturgeon which uses
both biological and behavioral (longitudinal)
data. In particular, we use data produced from a tracking study conducted in
the
May 14, Wednesday, 12pm
Professor Xiaofeng Shao, Department
of Statistics,
Title: Portmanteau tests in time series
Abstract: This
talk consists of two parts. In the first part, we will talk about testing for
white noise and its applications to goodness-of-fit of long memory time series
models. The limitation of the current asymptotic theory for portmanteau tests
will be pointed out and new theoretical results will be discussed. In the second
part, we will introduce generalized portmanteau
type test statistics in the frequency domain to test independence between two
stationary time series. Unlike the existing tests, each time series is allowed
to possess short memory, long memory or anti-persistence. Under the null
hypothesis of independence, the asymptotic null distributions of the proposed
statistics are standard normal. The results from a simulation study will also
be presented.
, Wednesday, 12pm
Professor
Title:
Abstract: