Friday,
September 26
CBCC112, 11:30 am
(refreshments at 11:15 am)
Prof. Jaechoul
Lee
Department of Mathematics
Boise State University
Title: Trends in
Extreme United States Temperatures
[Abstract] Extreme
temperatures have profound societal, ecological, and economic impacts. While
most scientists concur that average temperatures in the contiguous United
States since 1900 have warmed on aggregate, there is no a priori reason to
believe that temporal trends in averages and extremes will exhibit the same
patterns during this period. Indeed, under minor regularity conditions, the
sample mean and maximum of stationary time series are statistically
independent in large samples.
This talk presents trend
estimation methods for monthly maximum and minimum temperature time series
observed in the 48 conterminous United States over the last century. Previous
authors have suggested that minimum temperatures are warming faster than
maximum temperatures in the United States; such an aspect can be rigorously
investigated via the methods discussed in this study. Here, statistical
models with extreme value and change point features are used to estimate
trends and their standard errors. A spatial smoothing is then done to extract
general structure. The results show that monthly maximum temperatures are not
often greatly changing  perhaps surprisingly, there are many stations that
show some cooling. In contrast, the minimum temperatures show significant
warming. Overall, the southeastern United States shows the least warming
(even some cooling), and the western United States, northern Midwest, and New
England have experienced the most warming.
Friday,
October 17
CBCC112, 11:30 am
(refreshments at 11: 15am)
Prof. Guogen
Shan
Department of Environmental &
Occupational Health
University of Nevada, Las Vegas
Title: Exact Statistical
Inference for comparing
two independent Poisson rates
[Abstract] Two fundamental
problems for comparing two independent Poisson rates are considered: pvalue
calculation and confidence interval construction. Exact tests for pvalue
calculation are always preferable due to the guarantee of test size in small
to medium sample settings. Han (2008) compared the performance of partial
maximization pvalues based on the Wald test statistic, the likelihood
ratio test statistic, the score test statistic, and the conditional pvalue.
These four testing procedures do not perform consistently, as the results
depend on the choice of test statistics for general alternatives. We consider
the approach based on estimation and partial maximization, and compare these
to the ones studied by Han (2008) for testing superiority. The approach based
on partial maximization using the score test is recommended due to the
comparable performance and computational advantage in large sample settings.
Additionally, the approach based on estimation and partial maximization
performs consistently for all the three test statistics. We also examine
exact onesided confidence limits for the ratio of two independent Poisson
rates. The Buehler method is utilized to obtain exact limits, and this method
is used in conjunction with existing approximate limits. The exact limits
respect the coverage requirement, and they are as small as possible under
certain mild conditions.
Friday,
October 24
CBCC122, 11:30 am
(refreshments at 11: 15am)
Prof. Sanjib Basu
Statistics Division
Northern Illinois University
Title: A unified
competing risks cure rate model with application
in cancer survival data
[Abstract] A competing
risks framework refers to multiple risks acting simultaneously on a subject.
A cure rate postulates a fraction of the subjects to be cured or
failurefree, and can be formulated as a mixture model, or alternatively by a
bounded cumulative hazard model. We develop models that unify the
competing risks and cure rate approaches. The identifiability
of these unified models is studied in detail. We describe Bayesian
analysis of these models, and discuss conceptual, methodological and
computational issues related to model fitting and model selection. We
describe detailed applications in survival data from breast cancer patients
in the Surveillance, Epidemiology, and End Results (SEER) program of the
National Cancer Institute (NCI) of the United State.
Friday,
November 7
CBCC112, 11:00 am
(refreshments at 10: 45am)
Prof.
Usha Govindarajulu
Department of Epidemiology and
Biostatistics
State University of New York, Medical Center
Brooklyn, New York
Title: Frailty models:
Applications to biomedical and genetic studies
[Abstract] In this talk, we
provide a tutorial as an overview and general framework of frailty modeling
and estimation for multiplicative hazards models in the context of biomedical
and genetic studies. We will also briefly discuss other topics in frailty
models, such as diagnostic methods for model adequacy and inference in
frailty models. Some examples of analyses using multivariate frailty models
in a nonparametric hazards setting on biomedical datasets will be shown and
the implications of choosing to use frailty and relevance to genetic
applications will also be discussed.
Friday,
November 14
CBCC112, 11:30 am
(refreshments at 11:15am)
Prof. James Flegal
Department of Statistics
University of California, Riverside
Title: Relative
fixedwidth stopping rules for Markov chain Monte Carlo simulations
[Abstract] Markov chain
Monte Carlo (MCMC) simulations are commonly employed for estimating features
of a target distribution, particularly for Bayesian inference. A
fundamental challenge is determining when these simulations should stop.
We consider a sequential stopping rule that terminates the simulation
when the width of a confidence interval is sufficiently small relative to the
size of the target parameter. Specifically, we propose relative
magnitude and relative standard deviation stopping rules in the context of
MCMC. In each setting, we develop sufficient conditions for asymptotic
validity, that is conditions to ensure the simulation will terminate with
probability one and the resulting confidence intervals will have the proper
coverage probability. Our results are applicable in a wide variety of
MCMC estimation settings, such as expectation, quantile,
or simultaneous multivariate estimation. Finally, we investigate the
finite sample properties through a variety of examples and provide some
recommendations to practitioners.
