我国区域金融发展与经济增长的关系研究——基于面板数据的实证研究

运用1980—2004年间相应经济指标的面板数据对我国东、中、西部经济增长与金融发展进行了实证分析,结果表明,中西部地区更多的依靠信贷规模扩张来推动经济增长,而东部地区更多的依赖于金融效率的提升。     

信息化环境下审计内容与技术方法探讨

随着被审计单位各种信息系统的应用和会计信息化环境的逐渐形成,审计的内容与技术方法也发生了巨大的变化,审计内容转变为会计信息系统安全性审计和电子财务数据准确性审计两个方面。对会计系统安全性审计主要包括物理安全、技术安全和组织与管理安全几个方面;对电子财务数据准确性的审计一般分为数据采集、转换、清理与分析四个阶段。这些技术方法既有传统审计技术方法在信息化环境下的延伸,也有各种特定的IT审计技术。从长远上看,以智能审计专家知识库等IT审计技术将成为信息化环境下审计技术方法的未来发展趋势,对其方法进行研究具有很大的理论价值和现实意义。     

论高校专业资料室在教改中的特点及服务

本文从多方面对高校专业资料室的工作特点、内容及形式进行探讨 ,以使其在教改及科学研究中发挥更大的作用。     

中国高龄老人生活自理能力纵向动态研究

利用"中国老年健康长寿跟踪调查"1998年、2000年、2002年的跟踪数据分析中国高龄老人ADL的变动趋势及其影响因素。个体增长模型结果显示,高龄老人ADL状况随年龄增长出现了明显的下降趋势,ADL个体差异十分显著。个体之间的差异解释了ADL总差异的31.13%。经历三次追踪调查的高龄老人ADL均值较高,状况较好,下降速度较慢;基期具有3-4项残障的高龄老人,未来ADL下降最为迅速;基期ADL水平高的高龄老人,ADL下降较慢。从ADL的个体影响因素来看,对于基期ADL水平影响最大的变量是"年龄"、"疾病状况"以及"居住安排"等,而对观测期ADL变动速度影响最大的变量是"年龄"以及"过去是否经常从事体力劳动"等。     

Analysis of Competing Risks by Using Bayesian Smoothing

We consider the competing risks set-up. In many practical situations, the conditional probability of the cause of failure given the failure time is of direct interest. We propose to model the competing risks by the overall hazard rate and the conditional probabilities rather than the cause-specific hazards. We adopt a Bayesian smoothing approach for both quantities of interest. Illustrations are given at the end.     

GMM Estimation with persistent panel data: an application to production functions

This paper considers the estimation of Cobb-Douglas production functions using panel data covering a large sample of companies observed for a small number of time periods. GMM estimatorshave been found to produce large finite-sample biases when using the standard first-differenced estimator. These biases can be dramatically reduced by exploiting reasonable stationarity restrictions on the initial conditions process. Using data for a panel of R&Dperforming US manufacturing companies we find that the additional instruments used in our extended GMM estimator yield much more reasonable parameter estimates.     

A class of weighted dependence measures for bivariate failure time data

This paper considers a class of summary measures of the dependence between a pair of failure time variables over a finite follow-up region. The class consists of measures that are weighted averages of local dependence measures, and includes the cross-ratio-measure and finite region version of Kendall's τ; recently proposed by the authors. Two new special cases are identified that can avoid the need to estimate the bivariate survivor function and that admit explicit variance estimators. Nonparametric estimators of such dependence measures are proposed and are shown to be consistent and asymptotically normal with variances that can be consistently estimated. Properties of selected estimators are evaluated in a simulation study, and the method is illustrated through an analysis of Australian Twin Study data.     

Confidence intervals for secondary parameters following a sequential test

In sequential studies, formal interim analyses are usually restricted to a consideration of a single null hypothesis concerning a single parameter of interest. Valid frequentist methods of hypothesis testing and of point and interval estimation for the primary parameter have already been devised for use at the end of such a study. However, the completed data set may warrant a more detailed analysis, involving the estimation of parameters corresponding to effects that were not used to determine when to stop, and yet correlated with those that were. This paper describes methods for setting confidence intervals for secondary parameters in a way which provides the correct coverage probability in repeated frequentist realizations of the sequential design used. The method assumes that information accumulates on the primary and secondary parameters at proportional rates. This requirement will be valid in many potential applications, but only in limited situations in survival analysis.     

Small‐sphere distributions for directional data with application to medical imaging

We propose novel parametric concentric multi‐unimodal small‐subsphere families of densities for p ? 1 ≥ 2‐dimensional spherical data. Their parameters describe a common axis for K small hypersubspheres, an array of K directional modes, one mode for each subsphere, and K pairs of concentrations parameters, each pair governing horizontal (within the subsphere) and vertical (orthogonal to the subsphere) concentrations. We introduce two kinds of distributions. In its one‐subsphere version, the first kind coincides with a special case of the Fisher–Bingham distribution, and the second kind is a novel adaption that models independent horizontal and vertical variations. In its multisubsphere version, the second kind allows for a correlation of horizontal variation over different subspheres. In medical imaging, the situation of p ? 1 = 2 occurs precisely in modeling the variation of a skeletally represented organ shape due to rotation, twisting, and bending. For both kinds, we provide new computationally feasible algorithms for simulation and estimation and propose several tests. To the best knowledge of the authors, our proposed models are the first to treat the variation of directional data along several concentric small hypersubspheres, concentrated near modes on each subsphere, let alone horizontal dependence. Using several simulations, we show that our methods are more powerful than a recent nonparametric method and ad hoc methods. Using data from medical imaging, we demonstrate the advantage of our method and infer on the dominating axis of rotation of the human knee joint at different walking phases.     

Learning from a lot: Empirical Bayes for high‐dimensional model‐based prediction

Empirical Bayes is a versatile approach to “learn from a lot” in two ways: first, from a large number of variables and, second, from a potentially large amount of prior information, for example, stored in public repositories. We review applications of a variety of empirical Bayes methods to several well‐known model‐based prediction methods, including penalized regression, linear discriminant analysis, and Bayesian models with sparse or dense priors. We discuss “formal” empirical Bayes methods that maximize the marginal likelihood but also more informal approaches based on other data summaries. We contrast empirical Bayes to cross‐validation and full Bayes and discuss hybrid approaches. To study the relation between the quality of an empirical Bayes estimator and p, the number of variables, we consider a simple empirical Bayes estimator in a linear model setting. We argue that empirical Bayes is particularly useful when the prior contains multiple parameters, which model a priori information on variables termed “co‐data”. In particular, we present two novel examples that allow for co‐data: first, a Bayesian spike‐and‐slab setting that facilitates inclusion of multiple co‐data sources and types and, second, a hybrid empirical Bayes–full Bayes ridge regression approach for estimation of the posterior predictive interval.