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发布人:管理员       发布时间: 2016-07-05 15:49:51.0
学术会议通知:Workshop on Statistics and Stochastic Analysis

Workshop on Statistics and Stochastic Analysis


山东大学   201676-8




主办单位


山东大学中泰金融研究院


山东大学数学学院




学术委员会


  任:


彭实戈  山东大学


  员:


蔡宗武  University of Kansas


陈增敬  山东大学


陈振庆  University of Washington


    中国科学院


高付清  武汉大学


蒋文新  NorthwesternUniversity山东大学


    山东大学


栾贻会  山东大学


任艳霞  北京大学


宋仁明  University of Illinois


    澳门大学


张希承  武汉大学




组织委员会


  任:


    山东大学


  员:


嵇少林  山东大学


贾广岩  山东大学


    山东大学


栾贻会  山东大学


史春梅  山东大学


    山东大学


吴盼玉  山东大学


    

金支持


泰山学者海外特聘专家---蒋文新学科建设经费和科研经费


国家自然科学基金重点项目:金融数学中的若干随机分析问题的研究


 

会议举办地


山东省济南市山东大学中心校区(山大南路27号)知新楼B1238




 

会议日程安排

201677日,周四

 

主持人

报告人

报告题目

8:00-8:20

 

8:20-8:30

开幕式

8:30-9:10

 

蔡宗武

A New Test on Asset Return Predictability with Structural Breaks

9:10-9:50

陈振庆

Stability of Heat Kernel Estimates for Symmetric Non-local Dirichlet Forms

9:50-10:10

 

10:10-10:50

贾广岩

高付清

Cramer-type Moderate Deviations for Monotone Functions

10:50-11:30

 

Hyperbolic Anderson Model with Space-time Homogeneous Gaussian Noise

11:30-12:10

冯峥晖

Nonparametric Variable Selection and Its Application to Additive Models

12:10

 

14:00-14:40

栾贻会

蒋文新

Bayesian Generalized Method of Moments and Variable Selection

14:40-15:20

张希承

Stochastic Hamiltonian Flow with Singular Drifts

15:20-16:00

嵇少林

Recursive Utility Optimization with Nonsmooth Coefficients

16:00-16:30

 

16:30-17:10

 

 

A New Test for High Dimensional Regression Coefficients

17:10-17:50

陈增敬

Limit Theorems for Capacities

17:50

 

 

 

 

201678日,周五

 

主持人

报告人

报告题目

8:30-9:10

石玉峰

杨立坚

Oracally Efficient Estimation and Consistent Model Selection for Auto-regressive Moving Average Time Series with Trend

9:10-9:50

赵怀忠

Ergodicity of Periodic Stochastic Dynamical Systems and Spectral Characterisation

9:50-10:10

 

10:10-10:50

嵇少林

 

Leader-Follower Stochastic Differential Game with Asymmetric Information and Applications

10:50-11:30

 

Stationary Measures for Stochastic Lotka-Volterra Systems with Application to Turbulent Convection

11:30-12:10

 

Identification and Estimation of Generalized Linear Models with Parametric Nonignorable Missing Data Mechanism

12:10

 

14:00-14:40

高付清

宋仁明

Potential Theory of Subordinate Brownian Motions

14:40-15:20

 

Estimating and Testing High-dimensional Mediation Effects in Epigenetic Studies

15:20-16:00

郑海涛

Maximum Likelihood Method Under Equation Constraints for Case-Control Study   

16:00-16:30

 

16:30-17:10

 

任艳霞

Williams Decomposition for Superprocesses

17:10-17:50

 

Consistent Estimation for Distribution-uncertainty Regression Via Cross-sample and Semiparametric Methodologies

17:50

 



备注


6日入住学人大厦


7日、8日午餐:学人大厦自助餐厅


茶歇:知新楼B1135





报告摘要(按照报告人姓名拼音顺序排列)


A New Test onAsset Return Predictability with Structural Breaks

蔡宗武,厦门大学                zongwucai@gmail.com

 

Thispaper considers a predictive regression with a structural break at some unknowndate. We establish a testing procedure for testing asset returns predictabilityvia empirical likelihood method based on weighted score equations. The proposedempirical likelihood method works well theoretically in the sense that theWilks’s theorem holds. It is noteworthy that theoretical results are derivedunder a unified framework that it is unneeded to distinguish whether thepredicting regressors are stationary or nonstationary. Simulation experimentsare provided to confirm theoretical results and to illustrate the finite sampleperformance. As empirical analyses, we test for predictability on the S&P500 stock returns using the dividend-price ratio and the earnings-price ratio.Our empirical likelihood-based procedure suggests a clear improvement overexisting tests and should be used in practical applications.

 

 

 

 

 

Limit Theorems for Capacities

陈增敬,山东大学                zjchen@sdu.edu.cn                

 

Motivated by Ellsberg-type modelsand problems in mathematical finance, we investigate limit behaviors of two differentmodels: one is the very simple Bernoulli trials with ambiguity (or called Ellsberg-typemodel), and the other is sub-linear expectations arising

from mathematical finance.With a new notion of φ-convolution for randomvariables, we show that empirical averages obtained from a large number oftrials in both models have the same limit distribution. We also investigate therelation between this limit theorem and the weak law of large numbers fornonadditive probability, and show that they are equivalent under the assumptionofφ-convolution on random variables. Our results generalizewell-known laws of large numbers (LLNs), using the proofs that are completelydifferent from those in the existing literature. Finally, we discuss fourmodels which satisfy

the assumptions of our mainresults.

 

 

 

 

Stabilityof Heat KernelEstimatesfor SymmetricNon-localDirichlet Forms

陈振庆,Universityof Washington           zqchen@uw.edu    

 

In this talk, we consider symmetric jump processes of mixed-type  on metric measure spaces under general volume doublingcondition, and establish   stability of two-sided heat kernel estimatesand  heat kernel upper bounds. We obtain their stable equivalent characterizations in terms ofthe jumpingkernels, variants of cut-off Sobolev inequalities, and the Faber-Krahninequalities. In particular, we will present stability results of heatkernel estimates for $\alpha$-stable-like processes even with $\alpha\ge 2$when the underlying spaces have walk dimensions larger than $2$, which has beenone of the major open problems in this area.

 

Based on joint work with Takashi Kumagai and Jian Wang.

 

 

 

 

Identification and Estimation of Generalized Linear Models with Parametric Nonignorable Missing Data Mechanism

崔霞,广州大学                  cuixia@gzhu.edu.cn

 

Weaddress the problem of identifying and estimating generalized linear modelswhen the response values are nonignorably missing. A Logistic/Probit/Log-logpattern is taken to specify the missing data mechanism. In this situation,likelihood based on observed data may not be identi_able. In this article, weprove the models parameters areidentifiable under very mild conditions and then construct estimators based ona likelihood-based approach. The proposed estimators are shown to be consistentand asymptotically normal. Simulation studies demonstrate that the proposedinference procedure performs well in manysettings. We apply the proposed method to a dataset from research in a realstudy.

 

 

 

 

StationaryMeasures for Stochastic Lotka-Volterra Systems with Application to Turbulent Convection

董昭,中国科学院               dzhao@amt.ac.cn

 

Inthis talk I will give some ergodicity and nonergodicity for a class of stochasticLotka-Volterra systems as the noise intensity vanishes. The nonergodicity casecan be illustrated the turbulent characteristics. It is a phenomenon that theturbulence in a fluid layer heated from below and rotating about a verticalaxis is robust under stochastic disturbances. This is a joint work with LifengChen, Jifa Jiang, Lei Niu and Jianliang Zhai.

 

 

 

 

NonparametricVariable Selection and Its Application to Additive Models

 冯峥晖,厦门大学                zhfengwise@gmail.com

 

Formultivariate nonparametric regression models, existing variable selectionmethods with penalization require high-dimensional nonparametric approximationsin objective functions. When the dimension is high, none of methods withpenalization in the literature are readily available. Also, ranking andscreening approaches cannot have selection consistency when iterativealgorithms cannot be used due to inefficient nonparametric approximation. Inthis paper, a novel and easily implemented approach is proposed to makeexisting methods feasible for selection with no need of nonparametricapproximation. Selection consistency can be achieved. As an application toadditive regression models, we then suggest a two-stage procedure thatseparates selection and estimation steps. An adaptive estimation to thesmoothness of underlying components can be constructed such that theconsistency can be even at parametric rate if the underlying model is reallyparametric. Simulations are carried out to examine the performance of ourmethod, and a real data example is analyzed for illustration.

 

 

 

 

Cramer-type Moderate Deviations for Monotone Functions

高付清,武汉大学                  fqgao@whu.edu.cn             

 

We introduce Cramer-type moderate deviations fornonparametric maximum likelihood estimators of monotone functions and their applications.

 

 

 

 

 

Recursive Utility Optimization with Nonsmooth Coefficients

嵇少林,山东大学                   jsl@sdu.edu.cn             

 

This paper study the recursive utility maximizationproblem. We assume that the coefficients of both the wealth equations and therecursive utilities may be nonlinear and nonsmooth. After given an equivalentbackward formulation of our problem, we employ the variational formulation todeal with the nonsmooth coefficients. By the convexity duality method, theprimal "sup-inf" problem is translated to a dual minimization problemand the characterization of the saddle point of this game is derived. Finally,we obtain the optimal terminal wealth. To illustrate our results, three casesare explicitly worked out under some special assumptions. (joint work withXiaomin Shi)

 

 

 

 

Bayesian Generalized Method of Moments and VariableSelection

蒋文新,NorthwesternUniversity山东大学          mwj60208@yahoo.com

 

Animportant practice in statistics is to use robust likelihood-free methods, suchas the estimating equations, which only require assumptions on the momentsinstead of specifying the full probabilistic model. We propose a Bayesianapproach for such likelihood-free methods, based on (quasi-)posteriorprobabilities from the Bayesian Generalized Method of Moments (BGMM). Thisnovel concept allows us to incorporate two important advantages of a Bayesianapproach: the expressiveness of posterior distributions and the convenientcomputational method of MCMC. Many different applications are possible,including modeling the correlated longitudinal data, the quantile regression,and the graphical models based on partial correlation. We demonstratenumerically how our method works in these applications. Under mild conditions,we show that theoretically the BGMM can achieve the

posteriorconsistency for selecting the unknown true model, and that it possesses aBayesian version of the oracle property, i.e. the posterior distribution forthe parameter of interest is asymptotically normal and is as informative as ifthe true model were known. In addition, we show that the proposedquasi-posterior is valid to be interpreted as an approximate conditionaldistribution given a data summary.

 

Thisis the joint work with Cheng Li, Duke University. Wenxin Jiang acknowledges the Taishan Scholar program forsupporting his adjunct appointment at Shandong University.

 

 

 

 

Consistent Estimation for Distribution-uncertaintyRegression ViaCross-sample and SemiparametricMethodologies

 林路,山东大学                  linlu@sdu.edu.cn

 

Motivatingby the famous Ellsberg paradox, ambiguity (distribution-uncertainty) isquantitively and qualitatively important in behavior finance. We consider atype of distribution-uncertainty regressions that contains endogenous variableregression and semiparametric regression as its special cases. For such models,however, classical estimating function does involve infinitely many nuisanceparameters caused by the uncertain distributions. Consequently, the parametersof interest cannot be consistently estimated and the corresponding predictionis imprecise, even aimless. In this paper, cross-sample and semiparametrictechniques, together with a hidden-constant function, are proposed for dealingwith the infinitely many nuisance parameters. The resultant estimating functiononly contains the parameters of interest, and the estimators of them are alwaysconsistent and normally distributed with standard convergence rate. Moreover,the newly proposed methodologies can avoid the use of instrumental variable ornonparametric estimation even if actually the model under study containsendogenous variables or nonparametric components. On the other hand, themethodologies for numerical computation are simple, and the correspondingcomputation procedures are somewhat similar to those for thedistribution-certainty models. The main difference from the classicalregression analysis is that the estimation efficiency is related to the levelof distribution-uncertainty.

 

 

 

Estimating and Testing High-dimensional Mediation Effects in Epigenetic Studies

刘磊,NorthwesternUniversity                  Lei.liu@northwestern.edu

 

DNAmethylation is an important epigenetic mechanism to regulate gene expression.Genome-wide DNA methylation markers, e.g., measured by Illumina InfiniumHumanMethylation450 BeadChip, are ultra-high dimensional (around 480K). DNAmethylation markers may mediate pathways linking environmental exposures withhealth outcomes. However, there is a lack of analytical methods to identifysignificant mediators for high-dimensional mediation analysis. Based on sureindependent screening and minimax concave penalty (MCP) techniques, we havedeveloped a joint significance test for the mediation effect. We demonstrateits practical performance using Monte Carlo simulation studies and apply thismethod to investigate the extent to which DNA methylation markers mediate thecausal pathway from smoking to reduced lung function in the Normative AgingStudy.

 

 

 

 

WilliamsDecomposition for Superprocesses

任艳霞,北京大学                    yxren@math.pku.du.cn

 

Weare interested in a spinal decomposition for superprocesses involving theancestral lineage of the last individual alive (Williams' decomposition).

 

Forsuperprocesses with homogeneous branching mechanism, the spatial motion is independent of the genealogicalstructure. As a consequence, the law of the ancestral lineage of the lastindividual alive does not distinguish from the originalmotion. Therefore, in this setting, the description of the process at theextinction time may be deduced from Abraham and Delmas (2009) where no spatialmotion is taken into account.

 

Fornonhomogeneous branching mechanisms on the contrary, the law of the ancestrallineage of the last individual alive should depend on the distance to theextinction time. Using the Brownian snake, Delmas and H\'{e}nard (2013) providea description of the genealogy for superprocesses with non-homogeneousquadratic branching mechanism. 

 

Wewould like to find conditions such that the Williams' decomposition worksfor   superprocesses with eneralnon-homogeneous branching mechanisms. The talk is based on a working paper withRenming Song and Rui Zhang.

 

 

 

 

Hyperbolic Anderson Model with Space-time HomogeneousGaussian Noise

 宋健,香港大学                    txjsong@hku.hk

 

In this article, we study thestochastic wave equation in arbitrary spatial dimension d withmultiplicative noise, also known in the literature as the Hyperbolic AndresonModel. This equation is perturbed by a general Gaussian noise, which ishomogeneous in both space and time. We prove the existenceof a solution of this equation (in the Skorohod sense) and the Holder continuity ofits sample paths, under the same respective conditions on the spatial spectralmeasure of the noise as in the case of the white noise in time, regardless of the temporalcovariance function of the noise. This is joint work with R. M. Balan.

 

 

 

 

Potential Theory of Subordinate Brownian Motions

宋仁明,Universityof Illinois                    rsong@illinois.edu              

 

A subordinate Brownian motion can be obtained byreplacing the time parameter of a Brownian motion by an independent increasingLevy process (i. e., a subordinator). Subordinate Brownian motions form a largesubclass of Levy processes and they are very important in various applications.The generator of of a subordinate Brownian motion is a function of theLaplacian. In this talk, I will give a survey of some of the recent results inthe study of the potential theory of subordinate Brownian motions. Inparticular, I will present recent results on sharp two-sided estimates on thetransition densities of killed subordinate Brownian motions in smooth opensets, or equivalently, sharp two-sided estimates on the Dirichlet heat kernelsof the generators of subordinate Brownian motions.

 

 

 

 

Leader-Follower Stochastic Differential Game with Asymmetric Informationand Applications

熊捷,澳门大学                    xiong79@yahoo.com            

 

This talk is concerned with a leader-followerstochastic differential game with asymmetric information, where the informationavailable to the follower is based on some sub-$\sigma$-algebra of thatavailable to the leader. Such kind of game problems has

wide applications in finance, economics andmanagement engineering such as newsvendor problems, cooperative advertising andpricing problems. Stochastic maximum principles and verification theorems withpartial information will be presented. As an application, a linear-quadraticleader-follower stochastic differential game with asymmetric information isstudied. It is shown that the open-loop Stackelberg equilibrium admits a statefeedback representation if some system of Riccati equations is solvable. Thistalk is based on a joint work with Shi and Wang.

 

 

 

 

Oracally Efficient Estimation and Consistent Model Selection for Auto-regressive Moving Average Time Series with Trend

 杨立坚,清华大学                  yanglijian@tsinghua.edu.cn

   

Mosttime series that are encountered in practice contain non-zero trend, yettextbook approaches to time series analysis are typically focused on zero-meanstationary auto-regressive moving average (ARMA) processes. Trend is oftenestimated by ad hoc methods and subtracted from time series, and the residualsare used as the true ARMA noise for data analysis and inference, includingparameter estimation, lag selection and prediction. We propose a theoreticallyjustified two-step method to analyse time series consisting of a smooth trendfunction and ARMA error term, which is computationally efficient and easy forpractitioners to implement. The trend is estimated by B-spline regression, andthe maximum likelihood estimator based on residuals is shown to be oracallyefficient in the sense that it is asymptotically as efficient as if the truetrend function were known and then removed to obtain the ARMA errors. Inaddition, consistency of the Bayesian information criterion for model selectionis established for the detrended residual sequence. Finite sample performanceof the procedure is illustrated by simulation studies and real data analysis.

 

 

 

 

Stochastic Hamiltonian Flow with Singular Drifts

 张希承,武汉大学                XichengZhang@gmail.com

 

Inthis report, I will introduce recent progress about stochastic Hamitonian flowwith singular drifts.

 

 

 

 

Ergodicityof Periodic Stochastic Dynamical Systems and Spectral Characterisation

赵怀忠,LoughboroughUniversity                H.Zhao@lboro.ac.uk             

 

Ergodicity ofrandom dynamical systems in the random periodic regime where a periodicmeasure exists on a Polish space is obtained. In the Markovianrandom dynamical systems case, the idea of Poincar\'e sections isintroduced. It is proved  if the $\tau$-periodic measure is PS-ergodic,then it is ergodic. Moreover, if the infinitesimal generator of the Markovsemigroup has only $\{{2m\pi\over \tilde \tau}i\}_{m\in {\mathbb Z}}$ assimple eigenvalues on the imaginary axis, where $\tilde \tau={\tau\over k}$ forsome $k\in {\mathbb N}\setminus \{0\}$, then the periodic measure isPS-ergodic. Furthermore, if the semigroup on Poincar\'e sections  hasspectral gap, then the periodic measure is PS-mixing. The distinctionbetween random periodic and stationary regimes is given by a sufficientand necessary condition in terms of the spectral structure of theinfinitesimal generators. In particular if the $\tau$-periodic measure isPS-mixing, then the infinitesimal generator of the Markovian semigroup hasonly $\{{2m\pi\over \tilde \tau}i\}_{m\in {\mathbb Z}}$ as simpleeigenvalues on the imaginary axis, where $\tilde \tau={\tau\over k}$ for some$k\in {\mathbb N}\setminus \{0\}$,  if and only if the minimal periodof the periodic measure is no less than $\tilde \tau$.

 

 

 

 

Maximum Likelihood Method Under Equation Constraints for Case-Control Study

郑海涛,西南交通大学                htzheng@home.swjtu.edu.cn           

 

In case-control study, the restricted maximumlikelihood method does not work and the corresponding asymptotic propertieshave yet been studied. In this article, we considered the maximumlikelihood(ML) method for case-control study under equation constraints. Westudied the  asymptotic properties of therestricted ML estimators and the related likelihood ratio test converges to achi^2 distribution asymptotically. Simulation studies were performed toevaluate the proposed method and corresponding theoretical properties.

 

 

 

 

A New Test for High Dimensional RegressionCoefficients

 钟威,厦门大学                 wzhong@xmu.edu.cn

 

 

Testing a hypothesis for high dimensionalregression coefficients is of fundamental importance in the statistical theoryand applications. This paper aims to develop a new U-type test for coefficientsin high dimensional linear regression models based on an estimated U-statisticsof order two. With the aid of martingale central limit theorem, we prove thatthe asymptotic null distributions of the proposed test are normal under twodifferent distribution assumptions. The idea of refitted cross-validation (RCV)approach is utilized to reduce the bias of the sample variance in theestimation of the test statistic. We assess the finite-sample performance ofthe proposed test by examining its size and power via Monte Carlo simulationswhich show that the new test based on the RCV estimator of the varianceachieves higher powers, especially for the sparse cases. We also illustrate theapplication of the proposed test by an empirical analysis of a microarray dataset on Yorkshire gilts.

 

 

 

参加会议人员

        蔡宗武  University of Kansas

陈增敬  山东大学

陈振庆  Universityof Washington

    广州大学

    中国科学院

冯新伟  山东大学

冯峥晖  厦门大学

高付清  武汉大学

胡明尚  山东大学

黄宗媛  山东大学

黄伟桓  山东大学

嵇少林  山东大学

贾广岩  山东大学

蒋文新  NorthwesternUniversity,山东大学

兰玉婷  山东大学

    山东大学

李欣鹏  山东大学

    山东大学

林一伟  山东大学

    Northwestern University

栾贻会  山东大学

聂天洋  山东大学

彭实戈  山东大学

任艳霞  北京大学

时晓敏  山东大学

石玉峰  山东大学

    香港大学

宋仁明  Universityof Illinois

谭玉珍  山东大学

王法磊  山东大学

王汉超  山东大学

    山东大学

    山东大学

吴盼玉  山东大学

    山东大学

    澳门大学

    山东大学

许振宇  山东大学

杨立坚  清华大学

杨维强  山东大学

张国栋  山东大学

    山东大学

    山东大学

张希承  武汉大学

张自武  山东大学

赵怀忠  LoughboroughUniversity

郑海涛  西南交通大学

    厦门大学

宗高峰  山东财经大学



附件:Workshop on Statistics and Stochastic Analysis》会议程序册