＂两种＂规则下系统审计抽样时的抽样规模

通过抽样方式开展审计活动是现代审计方法发展的基本方向之一。系统审计抽样因其组织实施起来比较方便,因而在实际工作中得到广泛的应用。从抽样估计精度、调查费用以及统计决策角度对系统审计抽样时的抽样规模进行讨论,主要包括：给定估计允许误差时的抽样规模及在调查费用预算约束和抽样推断损失最小时的系统审计抽样规模等。     

发展中大国的发展优势与劣势

发展中大国是人口众多、国土面积广阔、自然资源丰富、经济发展潜力和经济规模比较大的发展中国家,除了发展中国家普遍存在的优势和劣势之外,还存在发展的任务更艰巨、地区平衡发展困难、贫富差距大且难消除、利益协调更困难、社会稳定更难维持、发展消耗资源绝对量大而且弥补困难、发展主要只能依靠本国力量等劣势,拥有人力资源和自然资源的比较优势、资金积累的优势、发展的空间优势、更突出的规模经济优势。应该采取多种有效措施,发挥优势,克服劣势。     

新医改下大型综合医院新型住院难问题研究

自2009年推行新医改以来,由于江苏部分地区在基层医疗机构率先实行了工资改革,出现了基层医务人员将患者推往大医院治疗的不良现象,从而造成了大医院住院难问题.老问题还未彻底解决,新问题已呈现.文章旨在通过对新医改后出现的大医院新型住院难现象进行研究,及时发现新医改过程中出现的问题,为全国新医改的顺利推进提供政策建议.     

探究式高校课堂有效教学模式设计与实验

针对高校课堂教学有效性问题,采用理论分析与案例实证方法,探讨探究式教学理念,剖析高校课堂有效教学存在的主要问题。提出从教学理念变更、课程准备情境化、精益问题设计、交流互动常态化、作业设计梯度化、课堂教学评价科学化等方面设计课堂教学模式,并基于小样本数据进行教学实验。研究表明:探究式课堂教学有利于提升高校课堂教学有效性。     

THE POTENTIAL OF INTEGRATED POPULATION MODELLING†

Recent work has shown how the Kalman filter can be used to provide a simple framework for the integrated analysis of wild animal census and mark‐recapture‐recovery data. The approach has been applied to data on a range of bird species, on Soay sheep and on grey seals. This paper reviews the basic ideas, and then indicates the potential of the method through a series of new applications to data on the northern lapwing, a species of conservation interest that has been in decline in Britain for the past 20 years. The paper analyses a national index, as well as data from individual sites; it looks for a change‐point in productivity, corresponding to the start of the decline in numbers, considers how to select appropriate covariates, and compares productivity between different habitats. The new procedures can be applied singly or in combination.     

一类多元Bernstein型算子加Jacobi权的逼近

本文首先用递归法定义一类新的多元Bernstein型算子，并给出其在一致逼近意义下的特征刻划．然后指出这一算子加权逼近的无界性．最后利用一类修正的加权K-泛函给出了该算子加Jacobi权逼近的特征刻划定理及其光滑性刻划．     

差分对振荡电路分析

通过对差分电路在大信号模型下的集电极电流的频谱分析 ,讨论了差分振荡电路的非线性失真、起振条件、输出电压等特性 ,并简要说明了差分振荡电路的调整方法     

On measuring sensitivity to parametric model misspecification

In settings where parametric inference is inconsistent under model misspecification, the discrepancy between correct and misspecified inferences is compared with the discrepancy between correct and misspecified models. To make the comparison tractable, large sample and small misspecification approximations are employed. The ratio of the approximate discrepancy between inferences to the approximate discrepancy between models is regarded as a relative measure of sensitivity to model misspecification. The maximum ratio over a family of correct distributions is determined as a measure of worst case sensitivity. As well, the distribution producing this maximum can be examined, to see how a particular combination of a parametric family and estimand is susceptible to model misspecifications.     

Estimation in conditional first order autoregression with discrete support

We consider estimation in the class of first order conditional linear autoregressive models with discrete support that are routinely used to model time series of counts. Various groups of estimators proposed in the literature are discussed: moment-based estimators; regression-based estimators; and likelihood-based estimators. Some of these have been used previously and others not. In particular, we address the performance of new types of generalized method of moments estimators and propose an exact maximum likelihood procedure valid for a Poisson marginal model using backcasting. The small sample properties of all estimators are comprehensively analyzed using simulation. Three situations are considered using data generated with: a fixed autoregressive parameter and equidispersed Poisson innovations; negative binomial innovations; and, additionally, a random autoregressive coefficient. The first set of experiments indicates that bias correction methods, not hitherto used in this context to our knowledge, are some-times needed and that likelihood-based estimators, as might be expected, perform well. The second two scenarios are representative of overdispersion. Methods designed specifically for the Poisson context now perform uniformly badly, but simple, bias-corrected, Yule-Walker and least squares estimators perform well in all cases.     

Maximum likelihood estimation for spatial models by Markov chain Monte Carlo stochastic approximation

We propose a two-stage algorithm for computing maximum likelihood estimates for a class of spatial models. The algorithm combines Markov chain Monte Carlo methods such as the Metropolis–Hastings–Green algorithm and the Gibbs sampler, and stochastic approximation methods such as the off-line average and adaptive search direction. A new criterion is built into the algorithm so stopping is automatic once the desired precision has been set. Simulation studies and applications to some real data sets have been conducted with three spatial models. We compared the algorithm proposed with a direct application of the classical Robbins–Monro algorithm using Wiebe's wheat data and found that our procedure is at least 15 times faster.