2011年7月5日，中国人民大学应用统计科学研究中心将举办“现代统计方法前沿研讨会”，会议将邀请来自美国、日本、英国的专家学者做学术报告。
2011年7月5日全天，会议上午9:30开始
中国人民大学明德主楼1016会议室
届时欢迎广大师生踊跃参加。
Handling Nonresponse in Longitudinal Studies：An Overview of Recent Research Developments
Nonresponse often occurs in longitudinal studies. When the nonresponse mechanism depends on the observed or unobserved values of the variable subject to nonresponse, statistical analysis is a great challenge. This research presentation provides an overview of recent research developments in this problem. Several semi-parametric approaches of handling nonresponse are introduced and assumptions under which these methods produce approximately unbiased and consistent estimators are discussed. Some empirical results are also presented.
Genetics Studies of Multivariate Traits
Identifying the risk factors for comorbidity is important in psychiatric research. Empirically, studies have shown that testing multiple, correlated traits simultaneously is more powerful than testing a single trait at a time in association analysis. Furthermore, for complex diseases, especially mental illnesses and behavioral disorders, the traits are often recorded in ordinal scales. In absence of covariates, nonparametric association tests have been developed for multiple (ordinal and/or quantitative) traits to study comorbidity. However, genetic studies generally contain measurements of some covariates that may confound the relationship between the risk factors of major interest (such as genes) and the outcomes. While it is relatively straightforward to include the covariates in the analysis of multiple quantitative traits, it is challenging for multiple ordinal traits. In this article, we propose a weighted test statistic based on a generalized Kendall`s tau to adjust for the effects of the covariates. We conducted simulation studies to compare the type I error and power of our proposed test with an existing test. The empirical results suggest that our proposed test increases the power of testing association when adjusting for the covariates. We further demonstrate the advantage of our test by analyzing a data set on genetics of alcoholism.
This presents a series of joint work with Ching-Ti Liu, Wensheng Zhu, Yuan Jiang, and Xueqin Wang.
国际资金循环统计监测体系与金融风险测度
随着中国经济在整个世界经济中比重的增大，中国的对外资金流量日益融入国际资金循环。因此中国经济面临着的一个不可回避的问题就是如何对资本项目实施监管及制度操作，进而实现中国金融的国际化。为此，建立国际资金循环的统计监测体系是势在必行的。IMF在2006年公布了金融稳定统计指南（Financial Soundness Indicator、FSI），实施了反映全球金融稳定的统计报告制度（Global Financial Stability Report），也在加强对金融危机早期预警系统(Early Warning System, EWS)的研究。FSI借鉴了三个基本测算框架，即SNA数据，国际会计准则，巴塞尔银行监管协定建立起了反映金融稳定的12项核心指标以及27项鼓励类指标。但由于分析目标的不同，FSI对于反映金融结构性问题以及评价金融风险有两个不足。一是FSI框架侧重于加总的部门信息，无法提供由于经济结构变化引起金融稳定的统计依据；二是FSI框架侧重于描述金融层面的稳定，但缺乏对于经济实体面以及海外因素变动引发金融风险的信息反馈。
本研究试图导入国际资金循环的概念，按照国际资金循环分析的理论框架，建立国际资金循环的统计观测体系。主要内容包括以下4点。第一，从国际资金循环的分析视角，重点考察对外资金循环对宏观经济的持续增长以及对金融体系稳定安全的影响，借鉴分析IMF的FSI体系以及美国对资本流出入统计监测方法(TIC)。第二，根据国际资金循环分析的理论框架，建立国际资金循环的统计监测体系。第三，从动态的系统将实体经济与金融经济相联系，国内资金流量与国际资本流动相结合，建立反映金融系统性风险的金融压力指数。第四，基于上述统计方法展开的实证分析，提出今后有待于解决的问题。
Joint modelling of mean and covariance structures for longitudinal data
When analysing longitudinal/correlated data, misspecification of covariance structures may lead to very inefficient estimators of parameters in the mean. In some circumstances, e.g., when missing data are present, it can result in very biased estimators of the mean parameters. Hence, correct models for covariance structures play a very important role. Like the mean, covariance structures can be actually modeled using linear or nonlinear regression model techniques. Various estimation methods were proposed recently to model the mean and covariance structures, simultaneously. In this talk, I will review these methods on joint modelling of the mean and covariance structures for longitudinal data, including linear, nonparametric regression models and semiparametric models. Real examples and simulation studies will be provided for illustration.
Some of work on high dimensional inference (ranging from signal detection, classification, to variable selection).
Sparse estimation of conditional graphical models with application to gene networks
In many applications the graph structure in a network arises from two sources: intrinsic connections and connections due to external effects. We introduce a sparse estimation procedure for graphical models that is capable of isolating the intrinsic connections by removing the external effects. Technically, this is formulated as a conditional graphical model, in which the external effects are modeled as predictors, and the graph is determined by the nonzero entries of the conditional precision matrix. We introduce two sparse estimators of the conditional precision matrix using reproducing kernel Hilbert space combined with lasso and adaptive lasso. We establish the sparse property, variable selection consistency, oracle property, and derive the explicit asymptotic distributions of the proposed estimators for a specific type of reproducing kernel. The methods are compared with sparse estimators for unconditional graphical models, and with the constrained maximum likelihood estimate that assumes a known graph structure. Finally, the methods are applied to a genetic data set to construct a gene network after removing the effects from single-nucleotide polymorphisms.
Oracle Inequality for Statistical Inverse Problems
In this talk we consider a sequence of hierarchical space model of inverse problems. The underlying function is estimated from indirect observations over a variety of error distributions including those that are heavy-tailed and may not even possess variances or means. The main contribution of this paper is that we establish some oracle inequalities for the inverse problems by using quantile coupling technique and an automatic selection principle for the nonrandom filters. This leads to the data-driven choice of weights. We also give an algorithm for its implementation.
Keywords: Inverse problem; Robust estimation; Oracle inequalities; Quantile coupling inequalities; Heavy-tailed distributions; Hierarchical sequence space model.
Inverse problem; Robust estimation; Oracle inequalities; Quantile coupling inequalities; Heavy-tailed distributions; Hierarchical sequence space model.