报告题目： 
MULTIPLE
TESTING VIA FDR_L FOR LARGESCALE IMAGING DATA 
报
告 人： 
冯进教授,美国堪萨斯大学 
时间地点： 
2011年7月2628,30日下午3:00
S712 
摘 要： 
In this series of lectures, I will first introduce two classes
of first order HamiltonJacobiBellman (HJB) equations in
the space of probability measures, and then develop a well
posedness theory for both the Cauchy and resolvent type problems.
In the first lecture, I will derive two Hamiltonians using
probabilistic large deviation theory applied to meanfield
models from statistical mechanics, provide their connections
with optimal controlled PDEs in space of measures, and explain
some a priori estimates at microscopic level using probability
theory.
In the second lecture, I will introduce necessary background
about optimal mass transportation theory and use the language
of such theory to prove uniqueness (comparison principle)
result for HJB equations involving one of the Hamiltonians
(the nicer one).
In the third lecture, I will improve the comparison principle
result in the last lecture by regularization method and obtain
continuity result of the solution under a reasonably weak
topology (the Wasserstein2 metric).
In the fourth lecture, I will introduce new ingredients to
show the uniqueness (comparison principle) of HJB equations
for the second Hamiltonian which has a strong singular term.
I will also explain the relation of such equation to the OnsagerJoyceMontgomery
theory of 2D turbulent vortex flows.

报告人简历： 


报告题目： 
MULTIPLE
TESTING VIA FDR_L FOR LARGESCALE IMAGING DATA 
报
告 人： 
Prof. Chunming Zhang,University of Wisconsin 
时间地点： 
2011年7月25日上午10:00
S1013 
摘 要： 
The multiple testing procedure plays an important role in
detecting the presence of spatial signals for largescale
imaging data. Typically, the spatial signals are sparse but
clustered. This paper provides empirical evidence that for
a range of commonly used control levels, the conventional
FDR procedure can lack the ability to detect statistical significance,
even if the pvalues under the true null hypotheses are independent
and uniformly distributed; more generally, ignoring the neighboring
information of spatially structured data will tend to diminish
the detection effectiveness of the FDR procedure. This paper
first introduces a scalar quantity to characterize the extent
to which the “lack of identification phenomenon” (LIP) of
the FDR procedure occurs.Second, we propose a new multiple
comparison procedure, called FDR_L, to accommodate the spatial
information of neighboring pvalues, via a local aggregation
of pvalues. Theoretical properties of the FDR_L procedure
are investigated under weak dependence of pvalues. It is
shown that the FDR_L procedure alleviates the LIP of the FDR
procedure, thus substantially facilitating the selection of
more stringent control levels. Simulation evaluations indicate
that the FDR_L procedure improves the detection sensitivity
of the FDR procedure with little loss in detection specificity.
The computational simplicity and detection effectiveness of
the FDR_L procedure are illustrated through a real dataset.

报告人简历： 


报告题目： 
Semiparametric
estimation under lengthbiased sampling 
报
告 人： 
Dr. CHIUNGYU HUANG,Biostatistics Research Branch,National
Institute of Allergy and Infectious Diseases (NIAID),National
Institutes of Health (NIH) 
时间地点： 
2011年7月13日下午4:00
S703 
摘 要： 
The prevalent cohort sampling design is frequently adopted
to study the natural history of a disease. Important examples
include epidemiological studies of disease etiology (Simon,
1980), cancer screening trials (Zelen and Feinleib, 1969),
and HIV prevalent cohort studies (Lagakos et al, 1988). In
addition to right censoring due to loss to followup, survival
data collected via prevalent sampling are subject to left
truncation, as those who fail before the recruitment time
are not observable.In this talk, I will present a pseudoprofile
likelihood method for survival data arising from lengthbiased
sampling, where the survival times are right censored and
left truncated by uniformly distributed random truncation
times. The proposed method does not involve estimation of
the censoring distribution; hence it is consistent even when
the censoring time is correlated with covariates. Simulation
studies show that the proposed estimator yields significant
improvement in efficiency over the popular maximum partial
likelihood estimator. To illustrate the proposed method, statistical
analyses using data from the Canadian Study of Health and
Aging (CSHA)and the CDC AIDS Blood Transfusion Study will
be reported. (This is joint work with Drs. Jing Qin and Dean
Follmann.)

报告人简历： 


报告题目： 
A
multiscale variational optimization problem with dynamical partial
differential equation constraints for applications of atmospheric
data assimilation 
报
告 人： 
谢元富
研究员,National Oceanic and Atmospheric Administration (NOAA) 
时间地点： 
2011年4月20日下午3:30
S703 
摘 要： 
Variational atmospheric data assimilation is a variational
minimization problem subject to a set of nonlinear partial
differential equations and is very important for improving
numerical weather prediction at various time scales from tornados
to hurricanes. It is very large and complex optimization problem.
In this presentation, we review a few important features of
a Space and Time Multiscale Analysis System (STMAS) developed
at NOAA Earth System Research Laboratory for improving variational
data assimilation and forecasts. We also review several outstanding
challenges in general data assimilation in variational or
Kalman filter. Some applications and numerical results will
be shown

报告人简历： 
谢元富研究员自1991年以来在美国国家海洋气象管理局的地球系统研究所从事数据同化和预报模式的研究。现任职称是研究员，目前担任若干项目的负责人，如，STMAS开发及应用项目，美国航空管理局暴风边界识别项目，NOAA观察系统模拟试验系统项目OSSE以及NOAA太平洋观察研究及预报试验项目。目前研究重点是其他在2000年提出的"时空多尺度同化系统（STMAS）"的开发及应用。STMAS是一个以微分方程为约束条件的大规模变分优化系统。控制变量个数在十的七到八次方以上。 

报告题目： 
Differential
Harnack inequality on Kaehler manifolds 
报
告 人： 
Prof.
Lei Ni, University of California at San Diego 
时间地点： 
2011年3月8日下午3:00
S712 
摘 要： 


