2013年

报告题目： 
Least squares estimation of threshold
models: a practical twostage procedure 
报
告 人： 
Dr. Dong Li, Hong Kong University
of Science and Technology, HK 
时间地点： 
2013年9月6日（星期五）下午4:00 思源楼712 
摘 要： 
Threshold models have attracted too
much attention and been widely used in econometrics, economics
and finance for modeling nonlinear phenomena. Its success is
partially due to its simplicity in terms of both modelfitting
and modelinterpretation. A popular approach to fit a threshold
model is the conditional least squares method. However, as modeling
data with threshold type of models the computational costs become
substantial. This paper proposes a novel method, twostage gridsearch
procedure, to quickly search the least squares estimate of the
threshold parameter in threshold models. Compared with the standard
gridsearch procedure used in literature, our new method extremely
reduces computational costs, which only requires leastsquares
operations of order O(\sqrt{n}). Its validity is also verified
theoretically. The performance of our procedure is evaluated
via Monte Carlo simulation studies in finite samples.


报告题目： 
Clinical Trials for Personalized
Medicine: New Designs and Statistical Inference 
报
告 人： 
Professor Feifang Hu,Department of
Statistics, University of Virginia and School of Statistics,
Renmin University of China 
时间地点： 
2012年12月13日（星期四）下午4:005:00 思源楼709室 
摘 要： 
In a short period of time, advances
in genetics has allowed scientists to identify genes (biomarkers)
that are linked with certain diseases. To translate these great
scientific findings into realworld products for those who need
them (personalized medicine), clinical trials play an essential
and important role. Personalized medicine is an approach that
will allow physicians to tailor a treatment regimen based on
an individual patient's characteristics (which could be biomarkers
or other covariates). To develop personalized medicine, we need
new designs for clinical trials so that genetics information
and other biomarkers can be incorporated to assist in treatment
selection.
This talk first provides a brief review of design and statistical
inference related with personalized medicine. Personalized medicine
raises some new challenges for the design of clinical trials
as: (1) more covariates (biomarkers) have to be considered,
and (2) particular attention needs to be paid to the interaction
between treatment and covariate. Then we discuss several new
families of designs for personal medicine. New techniques are
introduced to study the theoretical properties of the proposed
designs. Advantages of the proposed designs are demonstrated
through both theoretical and numerical studies. To deal with
the complex data structure arise in clinical trials of personalized
medicine, some further and important statistical issues are
discussed. 

报告题目： 
HighDimensional Sparse Additive
Hazards Regression 
报
告 人： 
Jinchi Lv, Assistant Professor, Marshall
School of Business, University of Southern California

时间地点： 
2012年8月8日下午3:15 思源楼703 
摘 要： 
Highdimensional sparse modeling
with censored survival data is of great practical importance,
as exemplified by modern applications in highthroughput genomic
data analysis and credit risk analysis. In this article, we
propose a class of regularization methods for simultaneous variable
selection and estimation in the additive hazards model, by combining
the nonconcave penalized likelihood approach and the pseudoscore
method. In a highdimensional setting where the dimensionality
can grow fast, polynomially or nonpolynomially, with the sample
size, we establish the weak oracle property and oracle property
under mild, interpretable conditions, thus providing strong
performance guarantees for the proposed methodology. Moreover,
we show that the regularity conditions required by the $L_1$
method are substantially relaxed by a certain class of sparsityinducing
concave penalties. As a result, concave penalties such as the
smoothly clipped absolute deviation (SCAD), minimax concave
penalty (MCP), and smooth integration of counting and absolute
deviation (SICA) can significantly improve on the L_1 method
and yield sparser models with better prediction performance.
We present a coordinate descent algorithm for efficient implementation
and rigorously investigate its convergence properties. The practical
utility and effectiveness of the proposed methods are demonstrated
by simulation studies and a real data example. This is a joint
work with Wei Lin. 

报告题目： 
Tuning Parameter Selection in
HighDimensional Penalized Likelihood 
报
告 人： 
Yingying Fan, Assistant Professor,
Marshall School of Business, University of Southern California 
时间地点： 
2012年8月8日下午2:00 思源楼703 
摘 要： 
Determining how to appropriately
select the tuning parameter is essential in penalized likelihood
methods for highdimensional data analysis. We examine this
problem in the setting of penalized likelihood methods for generalized
linear models, where the dimensionality of covariates p is allowed
to increase exponentially with the sample size n. We propose
to select the tuning parameter by optimizing the generalized
information criterion (GIC) with an appropriate model complexity
penalty. To ensure that we consistently identify the true model,
a range for the model complexity penalty is identified in GIC.
We find that this model complexity penalty should diverge at
the rate of some power of log p depending on the tail probability
behavior of the response variables. This reveals that using
the AIC or BIC to select the tuning parameter may not be adequate
for consistently identifying the true model. Based on our theoretical
study, we propose a uniform choice of the model complexity penalty
and show that the proposed approach consistently identifies
the true model among candidate models with asymptotic probability
one. We justify the performance of the proposed procedure by
numerical simulations and a gene expression data analysis. This
is a joint work with Professor Chengyong Tang. 

报告题目： 
NonConcave Penalized Likelihood
with NPDimensionality 
报
告 人： 
Jinchi Lv, Assistant Professor, Marshall
School of Business, University of Southern California

时间地点： 
2012年8月7日下午3:15 思源楼703 
摘 要： 
Penalized likelihood methods are
fundamental to ultrahigh dimensional variable selection. How
high dimensionality such methods can handle remains largely
unknown. In this paper, we show that in the context of generalized
linear models, such methods possess model selection consistency
with oracle properties even for dimensionality of NonPolynomial
(NP) order of sample size, for a class of penalized likelihood
approaches using foldedconcave penalty functions, which were
introduced to ameliorate the bias problems of convex penalty
functions. This fills a longstanding gap in the literature
where the dimensionality is allowed to grow slowly with the
sample size. Our results are also applicable to penalized likelihood
with the L_1penalty, which is a convex function at the boundary
of the class of foldedconcave penalty functions under consideration.
The coordinate optimization is implemented for finding the solution
paths, whose performance is evaluated by a few simulation examples
and the real data analysis. This is a joint work with Professor
Jianqing Fan. 

报告题目： 
Variable Selection in Linear Mixed
Effects Models 
报
告 人： 
Yingying Fan, Assistant Professor,
Marshall School of Business, University of Southern California 
时间地点： 
2012年8月7日下午2:00 思源楼703 
摘 要： 
This paper is concerned with the
selection and estimation of fixed and random effects in linear
mixed effects models. We propose a class of nonconcave penalized
profile likelihood methods for selecting and estimating important
fixed effects. To overcome the difficulty of unknown covariance
matrix of random effects, we propose to use a proxy matrix in
the penalized profile likelihood. We establish conditions on
the choice of the proxy matrix and show that the proposed procedure
enjoys the model selection consistency where the number of fixed
effects is allowed to grow exponentially with the sample size.
We further propose a group variable selection strategy to simultaneously
select and estimate important random effects, where the unknown
covariance matrix of random effects is replaced with a proxy
matrix. We prove that, with the proxy matrix appropriately chosen,
the proposed procedure can identify all true random effects
with asymptotic probability one, where the dimension of random
effects vector is allowed to increase exponentially with the
sample size. Monte Carlo simulation studies are conducted to
examine the finitesample performance of the proposed procedures.
We further illustrate the proposed procedures via a real data
example. This is a joint work with Professor Runze Li. 

报告题目： 
On the QMLE of a threshold double
AR model 
报
告 人： 
Shiqing Ling, Professor, Hong Kong
University of Science and Technology, China 
时间地点： 
2012年7月26日下午3:30 思源楼712 
摘 要： 
This paper proposes a threshold double
autoregressive model and studies its quasimaximum likelihood
estimation (QMLE). It is shown that the estimator is strongly
consistent and the estimated threshold is $n$consistent and
converges weakly to some functional of a twosided compound
Poisson process. The remaining parameters are asymptotically
normal. Our results include the asymptotic theory of the estimator
for the threshold AR model with ARCH errors and the threshold
ARCH model as special cases, each of which is also new in the
literature. A resampling method is presented to simulate the
limiting distribution of the estimated threshold, which can
be applied to construct confidence intervals of the threshold
parameter. Two portmanteautype statistics are also derived
for checking the adequacy of fitted model when either the error
is nonnormal or the threshold is unknown. Simulation studies
are conducted to assess the performance of the QMLE in finite
samples. The results are illustrated with an application to
the weekly closing prices of Hang Seng Index 

报告题目： 
What are structural eqution models? 
报
告 人： 
Kenneth A. Bollen, Professor,University
of North Carolina at Chapel Hill

时间地点： 
2012年7月24日下午3:00 思源楼712 
摘 要： 
Structural equation models (SEMs)
refer to procedures popular in the social and behavioral sciences
that are equipped to handle multiple equations with latent and
observed variables, multiple measures of concepts, and measurement
errors. From one perspective SEMs appear as a general statistical
model that includes factor analysis, simultaneous equations,
multiple regression, ANOVA, fixed and random effects models,
growth curve models, probit regressions, and other models as
special cases. But the “structural” in SEMs stands for the causal
assumptions that researchers bring to the model which are not
always part of these other statistical models. This presentation
provides a brief overview of latent variable SEMs. I present
the equations for SEMs and the major steps in modeling which
include model specification, determining the model implied moments,
establishing identification, estimating parameters, assessing
model fit, and respecifying poorly fitting models. I also provide
examples of SEMs. 

报告题目： 
Estimation of Changepoints in
ARMAGARCH/IGARCH and General Time Series Models 
报
告 人： 
Professor Shiqing Ling, Department
of Mathematics, HKUST 
时间地点： 
2012年7月16日下午3:30 思源楼703 
摘 要： 
This paper first develops a general
theory for estimating changepoints in a general class of linear
and nonlinear time series models. Based on a general objective
function, it is shown that the estimated changepoint converges
weakly to the location of the maxima of a doublesided random
walk and other estimated parameters are asymptotically normal.
When the magnitude d of changed parameters is small, it is shown
that the limiting distribution can be approximated by the known
distribution as in Yao (1987). This provides a channel to connect
our results with those in Picard (1985) and Bai, Lumsdaine and
Stock (1998), where the magnitude of changed parameters depends
on the sample size n and tends to zero as n approaches infinity.
We then focus on the selfweighted QMLE and the local QMLE of
structurechange ARMAGARCH/IGARCH models. The limiting distribution
of the estimated changepoint and its approximating distribution
are obtained. Some simulation results are reported and one real
example is given. 

报告题目： 
Estimating individualized treatment
rules using outcome weighted learning 
报
告 人： 
Donglin Zeng, Associate Professor,
University of North Carolina, Chapel Hill 
时间地点： 
2012年7月10日下午3:15 思源楼712 
摘 要： 
There is increasing interest in discovering
individualized treatment rules for patients who have heterogeneous
responses to treatment. In particular, one aims to find an optimal
individualized treatment rule which is a deterministic function
of patient specific characteristics maximizing expected clinical
outcome. In this paper, we first show that estimating such an
optimal treatment rule is equivalent to a classification problem
where each subject is weighted proportional to his or her clinical
outcome. We then propose an outcome weighted learning approach
based on the support vector machine framework. We show that
the resulting estimator of the treatment rule is consistent.
We further obtain a finite sample bound for the difference between
the expected outcome using the estimated individualized treatment
rule and that of the optimal treatment rule. The performance
of the proposed approach is demonstrated via simulation studies
and an analysis of chronic depression data 

报告题目： 
Estimating treatment effects with
treatment switching via semicompeting risks models: an application
to a colorectal cancer study 
报
告 人： 
Donglin Zeng, Associate Professor,
University of North Carolina, Chapel Hill 
时间地点： 
2012年7月10日下午2:00 思源楼712 
摘 要： 
Treatment switching is a frequent
occurrence in clinical trials, where, during the course of the
trial, patients who fail on the control treatment may change
to the experimental treatment. Analyzing the data without accounting
for switching yields highly biased and inefficient estimates
of the treatment effect. In this paper, we propose a class of
semiparametric semicompeting risks transition survival models
to accommodate treatment switches. Theoretical properties of
the proposed model are examined and an efficient expectationmaximization
algorithm is derived for obtaining the maximum likelihood estimates.
Simulation studies are conducted to demonstrate the superiority
of the model compared to the intenttotreat analysis and other
methods proposed in the literature. The proposed method is applied
to data from a colorectal cancer clinical trial. 

报告题目： 
Bayesian empirical likelihood
for quantile regression 
报
告 人： 
Xuming He, Professor, Department
of Statistics, University of Michigan 
时间地点： 
2012年7月9日下午3:00 思源楼703 
摘 要： 
Quantile regression is semiparametric
in the sense that no parametric likelihood is assumed in the
model. A working likelihood can be used, but the resulting posterior
may not have any validity for statistical inference. In this
talk we will introduce Bayesian empirical likelihood for quantile
regression, and show that it leads to asymptotically valid posterior
inference. In addition, this approach enables us to make use
of commonality across quantiles to improve efficiency of quantile
estimation. We will also introduce a notion of shrinking priors,
and demonstrate how this new framework can help explain the
efficiency gains of the Bayesian empirical likelihood method
over the usual quantile estimates. The talk is based on joint
work with Yunwen Yang (Drexel University). 

报告题目： 
Empirical likelihood inference
for the Cox model with timedependent coefficients 
报
告 人： 
Yichuan Zhao, Associate Professor,
Department of Mathematics and Statistics, Georgia State University 
时间地点： 
2012年7月6日下午3:00 思源楼712 
摘 要： 
The Cox model with timedependent
coefficients has been studied by a number of authors recently.
In this talk, we develop empirical likelihood (EL) pointwise
confidence regions for the timedependent regression coefficients
via local partial likelihood smoothing. The EL simultaneous
confidence bands for a linear combination of the coefficients
are also derived based on the strong approximation methods.
The EL ratio is formulated through the local partial loglikelihood
for the regression coefficient functions. Our numerical studies
indicate that the EL pointwise/simultaneous confidence regions/bands
have satisfactory finite sample performances. Compared with
the confidence regions derived directly based on the asymptotic
normal distribution of the local constant estimator, the EL
confidence regions are overall tighter and can better capture
the curvature of the underlying regression coefficient functions.
Two data sets, the gastric cancer data and the Mayo Clinic primary
biliary cirrhosis data, are analysed using the proposed method.
This is based on joint work with Yanqing Sun and Rajeshwari
Sundaram. 

报告题目： 
Large Volatility Matrix Estimation
for HighFrequency Financial Data 
报
告 人： 
Professor Yazhen Wang, University
of WisconsinMadison 
时间地点： 
2012年7月2日下午4:00 思源楼703 
摘 要： 
Volatilities of asset returns are
central to the theory and practice of asset pricing, portfolio
allocation, and risk management. In financial economics, there
is extensive research on modeling and forecasting volatility
up to the daily level based on BlackScholes, diffusion, GARCH,
stochastic volatility models and implied volatilities from option
prices. Nowadays, thanks to technological innovations, highfrequency
financial data are available for a host of different financial
instruments on markets of all locations and at scales like individual
bids to buy and sell, and the full distribution of such bids.
The availability of highfrequency data stimulates an upsurge
interest in statistical research on better estimation of volatility.
This talk will start with a review on lowfrequency financial
time series and highfrequency financial data. Then I will introduce
popular realized volatility computed from highfrequency financial
data and present my work on large volatility matrix estimation. 

报告题目： 
Joint Estimation of Multiple Graphical
Models 
报
告 人： 
Associate Professor, University of
Michigan 
时间地点： 
2012年7月2日下午3:00 思源楼703 
摘 要： 
Gaussian graphical models explore
dependence relationships between random variables, through estimation
of the corresponding inverse covariance matrices. In this paper
we develop an estimator for such models appropriate for data
from several graphical models that share the same variables
and some of the dependence structure. In this setting, estimating
a single graphical model would mask the underlying heterogeneity,
while estimating separate models for each category does not
take advantage of the common structure. We propose a method
which jointly estimates the graphical models corresponding to
the different categories present in the data, aiming to preserve
the common structure, while allowing for differences between
the categories. This is achieved through a hierarchical penalty
that targets the removal of common zeros in the inverse covariance
matrices across categories. We establish the asymptotic consistency
and sparsity of the proposed estimator in the highdimensional
case, and illustrate its superior performance on a number of
simulated networks. An application to learning semantic connections
between terms from webpages collected from computer science
departments is also included. This is joint work with Jian Guo,
Elizaveta Levina, and George Michailidis. 

报告题目： 
Personalized Treatment Selection
with Biomarkers 
报
告 人： 
Tianxi Cai, Professor of Biostatistics,
Department of Biostatistics, Harvard School of Public Health,
USA 
时间地点： 
2012年6月7日上午10:0011:30 思源楼712 
摘 要： 
Clinical trials that evaluate treatment
benefit focus primarily on estimating the average benefit. However,
a treatment reported to be effective may not be beneficial to
all patients. For example, the benefit of giving chemotherapy
prior to hormone therapy with Tamoxifen in the adjuvant treatment
of postmenopausal women with lymph node negative breast cancer
depends on the ERstatus. Due to the toxicity of chemotherapy,
it is crucial to identify patients who will and will not benefit
from chemotherapy. This gives rise to the need of accurately
predicting benefit based on important markers. In this research,
we propose a systematic, twostage estimation procedure for
the subjectlevel treatment differences for future patient's
disease management and treatment selections. To construct this
procedure, we first utilize a parametric or semiparametric
method to estimate individuallevel treatment differences and
use these estimates to create an index scoring system for clustering
patients. We subsequently estimate the average treatment difference
for each cluster of subjects via a nonparametric function estimation
method. Furthermore, pointwise and simultaneous interval estimates
are constructed to make inferences about such individualspecific
treatment differences. The new proposal is illustrated with
the data from an AIDS clinical trial and a randomized trial
for treating patients with stable coronary heart disease. 

报告题目： 
Risk Prediction with Biomarkers
under Complex Study Designs 
报
告 人： 
Tianxi Cai, Professor of Biostatistics,
Department of Biostatistics, Harvard School of Public Health,
USA 
时间地点： 
2012年6月6日上午10:0011:30 思源楼712 
摘 要： 
To evaluate the clinical utility
of new biomarkers for risk prediction, a crucial step is to
measure their predictive accuracy with prospective studies.
However, it is often infeasible to obtain marker values for
all study participants. The nested casecontrol (NCC) design
is a useful costeffective strategy for such settings. Under
the NCC design, markers are only ascertained for cases and a
fraction of controls sampled randomly from the risk sets. The
outcome dependent sampling generates a complex data structure
and therefore a challenge for analysis. Existing methods for
analyzing NCC studies focus primarily on association measures.
When there is a single marker of interest, we propose a class
of nonparametric estimators for commonly used accuracy measures.
Asymptotic theory for the proposed estimators were derived to
account for both the outcome dependent missingness and the correlation
induced by finite population sampling due to the NCC design.
When there are multiple markers under investigation, we extended
the proposed procedures to derive an optimal composite risk
score for prediction. We provided inference procedures for the
prediction accuracy of the risk score and as well as for making
comparisons between two risk scores. The new procedures were
illustrated with data from the Nurse’s Health Study to evaluate
the accuracy of biomarkers and genetic markers for predicting
the risk of developing Rheumatoid Arthritis. 

报告题目： 
Evaluating Clinical Utility of
Biomarkers for Prediction

报
告 人： 
Tianxi Cai, Professor of Biostatistics,
Department of Biostatistics, Harvard School of Public Health,
USA 
时间地点： 
2012年6月5日上午10:0011:30 思源楼712 
摘 要： 
Novel biomarkers have the great potential
to dramatically change the decision making process of modern
medicine. Recently there has been increased interest in the
use diagnostic or prognostic markers for accurately diagnosing
disease or predicting the risk of future clinical events. In
this talk, we introduce various concepts of accuracy measures
for quantifying the clinical utility of biomarkers under such
settings. When new biomarkers are introduced to improve the
diagnostic or prognostic accuracy of existing modalities, it
is important to quantify the incremental value of new markers.
We will also discuss various procedures for making inference
about such incremental values over an entire population and
also over various subpopulations. These concepts will be introduced
under various clinical settings and illustrated with clinical
studies 

报告题目： 
Local Polynomial Regression for Symmetric
Positive Definite Matrices

报
告 人： 
Prof.
Hongtu Zhu(University of North Carolina at
Chapel Hill, USA) 
时间地点： 
2010年9月21日10:00 思源楼1013 
摘 要： 
Local polynomial regression has received extensive attention
for the nonparametric estimation of regression functions when
both response and covariate are in Euclidean space. However,
little has been done when the response is in a Riemannian
manifold. We develop an intrinsic local polynomial regression
(ILPR) and its associated ILPR estimate for the analysis of
symmetric positive definite (SPD) matrices as responses that
lies in a Riemannian manifold with covariate in Euclidean
space. The primary motivation and application of the proposed
methodology is in computer vision and medical imaging. We
examine two commonly used metrics including the Riemannian
metric and the LogEuclidean metric on the space of SPD matrices.
Under each metric, we develop an associated crossvalidation
bandwidth selection method, and derive the asymptotic bias,
variance, and normality of the intrinsic local constant and
local linear estimators and compare their asymptotic mean
square errors. Simulation studies are further used to compare
the estimators under the two metrics and examine their finite
sample performance. We apply our method to detect the diagnostic
differences by smoothing diffusion tensors along fiber tracts
in a study of human immunodeficiency virus.


报告题目： 
On
the Estimation of Integrated Covariance Matrices of High Dimensional
Diffusion Processes 
报
告 人： 
Prof.
Yingying Li(Hong Kong University of Science and Technology)

时间地点： 
2010年6月13日15:0016:00 思源楼703 
摘 要： 
We
consider the estimation of integrated covariance matrices of
high dimensional diffusion processes by using high frequency
data. We start by studying the most commonly used estimator,
the realized covariance matrix (RCV). We show that in the high
dimensional case when the dimension p and the observation frequency
n grow in the same rate, the limiting empirical spectral distribution
of RCV depends on the covolatility processes not only through
the underlying integrated covariance matrix Sigma, but also
on how the covolatility processes vary in time. In particular,
for two high dimensional diffusion processes with the same integrated
covariance matrix, the empirical spectral distributions of their
RCVs can be very different. Hence in terms of making inference
about the spectrum of the integrated covariance matrix, the
RCV is in general \emph{not} a good proxy to rely on in the
high dimensional case. We then propose an alternative estimator,
the timevariation adjusted realized covariance matrix (TVARCV),
for a class of diffusion processes. We show that the limiting
empirical spectral distribution of our proposed estimator TVARCV
does depend solely on that of Sigma through a MarcenkoPastur
equation, and hence the TVARCV can be used to recover the empirical
spectral distribution of Sigma by inverting the MarcenkoPastur
equation, which can then be applied to further applications
such as portfolio allocation, risk management, etc..
This is based on Joint work with Xinghua Zheng..


2008年

报告题目： 
Onestep
Sparse Estimates in Nonconcave Penalized Likelihood Models 
报
告 人： 
Prof.
Runze Li (Associate Professor The Pennsylvania State University)

时间地点： 
2008年6月27日16:0017:40 思源楼703 
摘 要： 
Fan
and Li (2001) proposed a family of variable selection methods
via penalized likelihood using concave penalty functions. The
nonconcave penalized likelihood estimators enjoy the oracle
properties, but maximizing the penalized likelihood function
is computationally challenging, because the objective function
is nondifferentiable and nonconcave. In this article we propose
a new unified algorithm based on the local linear approximation
(LLA) for maximizing the penalized likelihood for a broad class
of concave penalty functions. Convergence and other theoretical
properties of the LLA algorithm are established. A distinguished
feature of the LLA algorithm is that at each LLA step, the LLA
estimator can naturally adopt a sparse representation. Thus
we suggest using the onestep LLA estimator from the LLA algorithm
as the final estimates. Statistically, we show that if the regularization
parameter is appropriately chosen, the onestep LLA estimates
enjoy the oracle properties with good initial estimators. Computationally,
the onestep LLA estimation methods dramatically reduce the
computational cost in maximizing the nonconcave penalized likelihood.
We conduct some Monte Carlo simulation to assess the finite
sample performance of the onestep sparse estimation methods.
The results are very encouraging.. 

报告题目： 
Quotient
Correlation: A Sample Based Alternative To Pearson's Correlation 
报
告 人： 
Prof.
Zhengjun Zhang (Princeton University, USA) 
时间地点： 
2008年6月27日10:4011:40 思源楼1013 
摘 要： 
The
quotient correlation is defined here as an alternative to Pearson's
correlation that is more intuitive and flexible in cases where
the tail behavior of data is important. It measures nonlinear
dependence where the regular correlation coefficient is generally
not applicable. One of its most useful features is a test statistic
that has high power when testing nonlinear dependence in cases
where the Fisher's $Z$transformation test may fail to reach
a right conclusion. Unlike most asymptotic test statistics,
which are either normal or $\chi2$, this test statistic has
a limiting gamma distribution (henceforth, the gamma test statistic).
More than the common usages of correlation, the quotient correlation
can easily and intuitively be adjusted to values at tails. This
adjustment generates two new concept  the tail quotient correlation
and the tail independence test statistics, which are also gamma
statistics. Due to the fact that there is no analogue of the
correlation coefficient in extreme value theory, and there does
not exist an efficient tail independence test statistic, these
two new concepts may open up a new field of study. In addition,
an alternative to Spearman's rank correlation, a rank based
quotient correlation, is also defined. The advantages of using
these new concepts are illustrated with simulated data and real
data analysis of internet traffic and asset returns. 

报告题目： 
Statistical
semiparametric detection of significant activation for brain
fMRI 
报
告 人： 
Prof.
Chunming Zhang (Associate Professor University of Wisconsin
) 
时间地点： 
2008年6月27日9:3010:30 思源楼1013 
摘 要： 
Functional
magnetic resonance imaging (fMRI) aims to locate activated regions
in human brains when specific tasks are performed. The conventional
tool for analyzing fMRI data applies some variant of the linear
model, which is restrictive in modeling assumptions. To yield
more accurate prediction of the timecourse behavior of neuronal
responses, the semiparametric inference for the underlying hemodynamic
response function is developed to identify significantly activated
voxels. Under mild regularity conditions, we demonstrate that
a class of the proposed semiparametric test statistics, based
on the local linear estimation technique, follow chisquared
distributions under null hypotheses for a number of useful hypotheses.
Furthermore, the asymptotic power functions of the constructed
tests are derived under the fixed and contiguous alternatives.
Simulation evaluations and real fMRI data application suggest
that the semiparametric inference procedure provides more efficient
detection of activated brain areas than the popular imaging
analysis tools AFNI and FSL. 

2007年

报告题目： 
Challenge
of Dimensionaly in Classifications and Feature Selection 
报
告 人： 
Prof.
Jianqing Fan(Princeton University, USA) 
时间地点： 
2007年12月27日9:3010:30 思源楼703 
摘 要： 


报告题目： 
Regression
Analysis of Longitudinal Data with Outcome Dependent Observation
and Followup Times 
报
告 人： 
Prof.(Tony)
Jianguo Sun (University of Missouri, USA ) 
时间地点： 
2007年7月13日16:0017:00
思源楼703 
摘 要： 
Longitudinal
data frequently occur in many studies such as longitudinal followup
studies. To develop statistical methods and theory for the analysis
of them, independent or noninformative observation and censoring
times are typically assumed, which naturally leads to inference
procedures conditional on observation and censoring times (Diggle
et al., 1994; Lin and Ying, 2001). In many situations, however,
this may not be true or realistic. That is, longitudinal responses
may be correlated with observation times as well as censoring
time. This paper considers the analysis of longitudinal data
where these correlations may exist and a joint modeling approach
that uses some latent variables to characterize the correlations
is proposed. For inference about regression parameters, estimating
equation approaches are developed and both large and final sample
properties of the proposed estimators are established. The ethodology
is applied to a bladder cancer study that motivated this nvestigation. 

报告题目： 
AGGREGATION
OF NONPARAMETRIC ESTIMATORS FOR VOLATILITY MATRIX 
报
告 人： 
Jianqing
Fan, Yingying Fan and Jinchi Lv (Princeton University) 
时间地点： 
2007年6月25日16:0017:30
思源楼712 
摘 要： 
An
aggregated method of nonparametric estimators based on timedomain
and statedomain estimators is proposed and studied. To attenuate
the curse of dimensionality, we propose a factor modeling strategy.
We first investigate the asymptotic behaviors of nonparametric
estimators of the volatility matrix in the time domain and in
the state domain. The asymptotic normality is separately established
for nonparametric estimators in the time domain and state domain.
These two estimators are asymptotically independent. Hence,
they can be combined, through a dynamic weighting scheme, to
improve the efficiency of the estimated volatility matrix. The
optimal dynamic weights are derived and it is shown that the
aggregated estimator uniformly dominates the volatility matrix
estimators using timedomain or statedomain smoothing alone.
A simulation study, based on an essentially affine model for
the term structure, is conducted and it demonstrates convincingly
that the newly proposed procedure outperforms both time and
statedomain estimators. Empirical studies endorse further the
advantages of our aggregated method 

报告题目： 
Analysis
of Longitudinal Data with Semiparametric Estimation of Covariance
Function 
报
告 人： 
Runze
Li: Associate Professor (The Pennsylvania State University)

时间地点： 
2007年5月18日15:3016:30
晨兴中心605 
摘 要： 
Improving
efficiency for regression coefficients and predicting trajectories
of individuals are two important aspects in analysis of longitudinal
data. Both involve estimation of the covariance function. Yet,
challenges arise in estimating the covariance function of longitudinal
data collected at irregular time points. A class of semiparametric
models for the covariance function is proposed by imposing a
parametric correlation structure while allowing a nonparametric
variance function. A kernel estimator is developed for the estimation
of the nonparametric variance function. Two methods, a quasilikelihood
approach and a minimum generalized variance method, are proposed
for estimating parameters in the correlation structure. We introduce
a semiparametric varying coefficient partially linear model
for longitudinal data and propose an estimation procedure for
model coefficients by using a profile weighted least squares
approach. Sampling properties of the proposed estimation procedures
are studied and asymptotic normality of the resulting estimators
is established. Finite sample performance of the proposed procedures
is assessed by Monte Carlo simulation studies. The proposed
methodology is illustrated by an analysis of a real data example. 

报告题目： 
Accelerated
Life and Degradation Models with Dynamic Environment 
报
告 人： 
Prof.Mikhail
Nikulin (Statistique Mathématique et ses Applications, Victor
Segalen University) 
时间地点： 
2006年12月12日16:0017:00
思源楼712 
摘 要： 
We
consider here the statistical models with dynamic environment
describing dependence of the lifetime distribution on the timedependent
explanatory variables. Such models are used in reliability and
survival analysis to study the reliability of aging biotechnical
system, in dependence on their longevity, fatigue and degradation
under different conditions of exploration. The reliability theory
gives a general approach for construction of efficient statistical
models in terms of failure rates to study aging and degradation
problems in different areas such as industrial engineering and
technology, biophysics, biology, demography, radiobiology, genetics,
biostatistics, survival analysis, business and finance, etc...
We shall discuss the problems of statistical modelling and of
choice of design in accelerated life testing to obtain the statistical
estimators of the main reliability characteristics of aging
systems. 

报告题目： 
Nonlinear
Dependency and Its Application 
报
告 人： 
Wei
Gang (魏刚).(School of Mathematics and System Sciences Shandong
University ) 
时间地点： 
2006年11月17日16:0017:00
思源楼703 
摘 要： 
The
normal distribution and the linear model have been taken as
the central part classical statistic inference in both theory
and application. In the last decade, with the demand from the
social, medical, and industrial sciences, the nonlinear dependency
characterized by the partial ordering, copula construction,
and nonparametric dependency have shown great potentials in
their theoretical challenges and applied statistics. In this
talk, we particularly demonstrate the rich mathematical structures
constructed with the aid of copula analysis and some simple
but important applications of such nonclassical statistical
inference techniques. 

报告题目： 
TreeStructured
Survival Analysis Based on Variance of Survival Time 
报
告 人： 
Hua
Jin, Ph.D.(School of Mathematical Sciences, South China Normal
University) 
时间地点： 
2006年10月19日10:30 
摘 要： 
Tree structured
survival analysis (TSSA) is a popular alternative to the Cox
proportional hazards regression in medical research of survival
data. Several methods for constructing a tree of different
survival profiles have been developed, including one based
on twosample logrank test statistics and martingale type
residuals.
Lu, Jin and Mi
used variance of restricted mean lifetimes as an index of
degree of separation (DOS) to measure the efficiency in separations
of survival profiles by a classification method. They proposed
a hypothesis testing procedure for comparison of two classification
rules, especially for noninferiority test.
Our objective
here is to explore the use of DOS in TSSA. We propose an algorithm
in a similar fashion to the least square regression tree for
survival analysis. We apply the proposed method to prospective
cohort data from the Study of Osteoporotic Fracture that motivated
our research and then compare our classification rule to those
rules based on the logrank statistics and martingale residuals.


报告题目： 
Bayesian
Methods for Inferring Epistasis 
报
告 人： 
Prof.
Jun Liu (Department of Statistics, Harvard University) 
时间地点： 
2006年7月24日（周一）
下午2:00 思源楼712 
摘 要： 
I
will discuss a Bayesian approach in detecting multilocus interactions
(Epistasis) for casecontrol association studies. Existing methods
are either of low power or computationally infeasible when facing
of a large number of markers. Using MCMC sampling techniques,
the method can efficiently detect interactions among thousands
of markers. I will also discuss the issue of statistical significance
and how to adjust multiple comparisons in this situation (much
of these are conjectures, though). 

报告题目： 
Embracing
Statistical Challenges in the Information Technology Age 
报
告 人： 
Prof.
Bin Yu(Department of Statistics, University of California, Berkeley
) 
时间地点： 
2006年7月20日（周四） 上午10：00 思源楼703 
摘 要： 


报告题目： 
Bayesian
Hierarchical Modeling for Integrating Lowaccuracy and Highaccuracy
Experiments 
报
告 人： 
Prof.Jeff
Wu (Georgia Institute of Technology School of Industrial and
Systems Engineering ) 
时间地点： 
2006年7月14日（周五）
上午10：00 思源楼712 
摘 要： 
Standard
practice in analyzing data from different types of experiments
is to treat data from each type separately. By borrowing strength
across multiple sources, an integrated analysis can produce
better results. Careful adjustments need to be made to incorporate
the systematic differences among various experiments. To this
end, some Bayesian hierarchical Gaussian process models (BHGP)
are proposed. The heterogeneity among different sources is accounted
for by performing flexible location and scale adjustments. The
approach tends to produce prediction closer to that from the
highaccuracy experiment. The Bayesian computations are aided
by the use of Markov chain Monte Carlo and Sample Average Approximation
algorithms. The proposed method is illustrated with two examples:
one with detailed and approximate finite elements simulations
for mechanical material design and the other with physical and
computer experiments. 

报告题目： 
Fast
Functional MRI 
报
告 人： 
Prof.
CunHui Zhang (Department of Statistics, Rutgers University,
USA ) 
时间地点： 
2006年6月22日（星期四)
下午 4:005:00 思源楼703 
摘 要： 
We
develop fast functional MRI methods to improve the timeresolution
of the current functional MRI technology by sampling a small
fraction of the Fourier transform of the spin density, and using
a prolate wave filter to approximately obtain, not the usual
susceptibility map, but instead the integral of this quantity
over regions of interest in the brain at successive timepoints.
The aim of this space/time tradeoff is to obtain, at high timeresolution,
the total activity in these regions which processes the specific
stimulus/task, and more important in studying higher cognition,
the sequence of occurrences of these processes. An fMRI experiment
will be reviewed and discussed. This is joint work with Gary
Glover, Martin Lindquist and Larry Shepp. 

报告题目： 
Statistical
Challenges with High Dimensionality in Feature Selection 
报
告 人： 
Prof.
Jianqing Fan (Princeton University, USA ) 
时间地点： 
2006年6月2日（星期五)
上午 10:0011:00 思源楼712 
摘 要： 
Technological
innovations have revolutionized the process of scientific research
and knowledge discovery. The availability of massive data and
challenges from frontiers of research and development have reshaped
statistical thinking, data analysis and theoretical studies.
The challenges of highdimensionality arise in diverse fields
of sciences and the humanities, ranging from computational biology
and health studies to financial engineering and risk management.
In all of these fields, variable selection and feature extraction
are crucial for knowledge discovery. We first give a comprehensive
overview of statistical challenges with high dimensionality
in these diverse disciplines. We then approach the problem of
variable selection and feature extraction using a unified framework:
penalized likelihood methods. Issues relevant to the choice
of penalty functions are addressed. We demonstrate that for
a host of statistical problems, as long as the dimensionality
is not excessively large, we can estimate the model parameters
as well as if the best model is known in advance. The persistence
property in risk minimization is also addressed. The applicability
of such a theory and method to diverse statistical problems
is demonstrated. Other related problems with highdimensionality
are also discussed. 

报告题目： 
Semi/Nonparametric
Dynamic Quantile Regression Models and Their Applications 
报
告 人： 
Prof.
Zongwu Cai (Department of Mathematics and Statistics & Department
of Economics, University of North Carolina, Charlotte, USA) 
时间地点： 
2006年4月8日（星期六)
下午 4:005:00 思源楼712 
摘 要： 
In
this talk, first I will briefly review some semiparametric and
nonparametric regression models for time series data and their
applications such as valueatrisk. In particular, I will focus
on a class of smooth coefficient quantile regression time series
models based on some applications. We employ a local linear
fitting scheme to estimate the smooth coefficients in the quantile
framework. The programming involved in the local linear quantile
estimation is relatively simple and it can be modified with
few efforts from the existing programs for the linear quantile
model. We derive the local Bahadur representation of the local
linear estimator for alphamixing time series and establish
the asymptotic normality of the resulting estimator. The asymptotic
behaviors of the estimator at the boundaries are examined. A
comparison of the local linear quantile estimator with the local
constant estimator is presented. A simulation study is carried
out to illustrate the performance of the estimates. An empirical
application of the model to the exchange rate time series data
and the wellknown Boston house price data further demonstrates
the potential of the proposed modeling procedures. 

报告题目： 
Additive
models for spatial processes 
报
告 人： 
Dag
Tjostheim 院士（Department of Mathematics,University of Bergen,
Norway） 
时间地点： 
2006年4月11日（星期二）下午 4:005:00 思源楼712

摘 要： 


报告题目： 
Estimating
Marginal Survival Under Dependent Censoring 
报
告 人： 
Donglin
Zeng (Assistant Professor)(Department of Biostatistics, University
of North Carolina (Chapel Hill) ) 
时间地点： 
2006年4月13日（星期四）下午
2:003:00 思源楼712 
摘 要： 
One
goal in survival analysis of right censored data is to estimate
marginal survival function in the presence of dependent censoring.
When many auxiliary covariates are sufficient to explain the
dependent censoring, estimation based on either semiparametric
model or nonparametric model of the conditional survival function
can be problematic due to the highdimensionality of the auxiliary
information. In this paper, we use two working models to condense
these highdimensional covariates in dimension reduction; then
an estimate of the marginal survival function can be derived
nonparametrically in a lowdimension space. We show that such
an estimator has the following double robust property: when
either working model is correct, the estimator is consistent
and asymptotically Gaussian; when both working models are correct,
the asymptotic variance attains the efficiency bound. 

报告题目： 
Maximum
Likelihood Estimation in Semiparametric Transformation Models
for Counting Processes 
报
告 人： 
Donglin
Zeng (Assistant Professor) (Department of Biostatistics, University
of North Carolina (Chapel Hill) ) 
时间地点： 
2006年4月18日（星期二）下午
2:003:00 思源楼712 
摘 要： 
A
class of semiparametric transformation models is proposed to
characterize the effects of possibly timevarying covariates
on the intensity functions of counting processes. The class
includes the proportional intensity model and linear transformation
models as special cases. Nonparametric maximum likelihood estimators
are developed for the regression parameters and cumulative intensity
functions of these models based on censored data. The estimators
are shown to be consistent and asymptotically normal. The limiting
variances for the estimators of the regression parameters achieve
the semiparametric efficiency bounds and can be consistently
estimated. The limiting variances for the estimators of smooth
functionals of the cumulative intensity function can also be
consistently estimated. Simulation studies reveal that the proposed
inference procedures perform well in practical settings. Two
medical studies are provided. 

报告题目： 
Semiparametric
Transformation Models for Survival Data with a Cure Fraction 
报
告 人： 
Donglin
Zeng (Assistant Professor) (Department of Biostatistics, University
of North Carolina (Chapel Hill) ) 
时间地点： 
2006年4月19日（星期三）下午
2:003:00 思源楼712 
摘 要： 
We
propose a class of transformation models for survival data with
a cure fraction. The class of transformation models is motivated
by biological considerations, and it includes both the proportional
hazards and the proportional odds cure models as two special
cases. An efficient recursive algorithm is proposed to calculate
the maximum likelihood estimators. Furthermore, the maximum
likelihood estimators for the regression coefficients are shown
to be consistent and asymptotically normal, and their asymptotic
variances attain the semiparametric efficiency bound. Simulation
studies are conducted to examine the finite sample properties
of the proposed estimators. The method is illustrated on data
from a clinical trial involving the treatment of melanoma. 