濮城油田沙二上1砂组末端扇沉积体系研究

曲流河、辫状河、网状河和若干三角洲的沉积模式目前已成为对比和解释古河流岩石层序的标准框架。但是，近来的野外资料和钻井资料证明，应该把末端扇模式加入上述沉积模式表中。末端扇的作用、地貌、有关的沉积物及沉积学特征相比河流体系及三角洲体系差距较大。通过大量的岩芯观察和描述，结合测井资料、区域地质资料及各项分析鉴定资料等进行综合分析，对东濮凹陷濮城油田沙二上亚段1砂组的沉积特征与沉积相进行了较全面的分析，认为该地区主要为末端扇沉积体系，在对末端扇的岩性、沉积相序、沉积微相特征分析的基础上，研究了末端扇的识别标志并建立了末端扇的沉积模式。     

农村居民看病难、看病贵原因实证分析

利用扩展的线性支出系统模型(ELES),采用最小二乘法估计计算了我国农村居民的短期医疗服务需求价格弹性。研究结果表明:农村居民的短期医疗服务需求价格缺乏弹性。由于农村居民的短期医疗服务需求价格缺乏弹性,加上我国目前医疗体制存在的以药养医和医疗机构的逐利行为以及医疗市场缺乏有效的监管等问题,导致农村居民看病难、看病贵。解决这个问题,需要国家增加对卫生领域的投入,降低农民看病时自费的比例,增强卫生市场的竞争性,适当使医疗卫生资源向农村地区倾斜,并逐渐把医疗机构办成非盈利机构。     

Backfitting Random Varying-Coefficient Models with Time-Dependent Smoothing Covariates

Abstract.  In this paper, we propose a random varying-coefficient model for longitudinal data. This model is different from the standard varying-coefficient model in the sense that the time-varying coefficients are assumed to be subject-specific, and can be considered as realizations of stochastic processes. This modelling strategy allows us to employ powerful mixed-effects modelling techniques to efficiently incorporate the within-subject and between-subject variations in the estimators of time-varying coefficients. Thus, the subject-specific feature of longitudinal data is effectively considered in the proposed model. A backfitting algorithm is proposed to estimate the coefficient functions. Simulation studies show that the proposed estimation methods are more efficient in finite-sample performance compared with the standard local least squares method. An application to an AIDS clinical study is presented to illustrate the proposed methodologies.     

对基础研究投入问题的思考

文章首先从公共财政的角度对基础研究的投入进行分析,其次从布什和斯托克斯的科研模式角度对基础研究的投入进行分析。在此基础上对加大我国基础研究的投入提出了相关建议。     

Local influence in multilevel models

The authors study the local influence of observations in multilevel regression models. To this end, they perturb simultaneously the variances, responses and design matrix. To measure the local change caused by these perturbations, they use generalized Cook statistics for the fixed and random parameter estimates. Closed form local influence measures also allow them to assess the joint influence of various observations. They suggest a simple computation method and illustrate their results using two examples.     

城市景观河流生态修复的产出研究及有效性可靠性检验——基于上海城市内河水质改善价值评估的实证分析

意愿价值评估法(CVM)为具有显著外部性的生态环境服务的价值评估提供可能,但只有经过有效性和可靠性检验的CVM成果才能应用于环境公共政策与治理。本文以CVM评价上海市城市景观内河——漕河泾港的生态恢复的产出,在国内经常采用的线形对数模型基础上,加入二值响应的Logit概率模型对受访者社会经济变量进行回归分析,验证本次CVM应用的理论有效性。回归中首次引入反映我国特殊社会结构的户籍变量、收入差距变量两者的交互项,并纳入间接反映环境问题历史成因的居民沿河居住期变量,以验证调查结果与一般经济理论的相容性和与我国特殊社会构成、经济态势以及环境历史成因的吻合性。以预调查和正式调查为试验—复试样本验证了研究结果的可靠性。结果表明:平均支付意愿是160元/(a.户),改善漕河泾水环境的年经济效益至少在6.1×106元。     

Estimating household structure in ancient China by using historical data: a latent class analysis of partially missing patterns

Summary.  Social data often contain missing information. The problem is inevitably severe when analysing historical data. Conventionally, researchers analyse complete records only. Listwise deletion not only reduces the effective sample size but also may result in biased estimation, depending on the missingness mechanism. We analyse household types by using population registers from ancient China (618–907 AD) by comparing a simple classification, a latent class model of the complete data and a latent class model of the complete and partially missing data assuming four types of ignorable and non-ignorable missingness mechanisms. The findings show that either a frequency classification or a latent class analysis using the complete records only yielded biased estimates and incorrect conclusions in the presence of partially missing data of a non-ignorable mechanism. Although simply assuming ignorable or non-ignorable missing data produced consistently similarly higher estimates of the proportion of complex households, a specification of the relationship between the latent variable and the degree of missingness by a row effect uniform association model helped to capture the missingness mechanism better and improved the model fit.     

Modelling heterogeneous space–time occurrences of earthquakes and its residual analysis

Summary. Earthquake intensities are modelled as a function of previous activity whose specific form is based on established empirical laws in seismology, but whose parameter values can vary from place to place. This model is used for characterizing regional features of seismic activities in and around Japan, and also for exploring regions where the actual seismicity rate systematically deviates from that of the modelled rate.     

如何行成关系数据模型到面向对象数据模型的映射

关系数据模型是基于记录的基本数据模型;面向对象数据模型是基于对象的基本数据模型;两种数据模型在某种程度上应存在相互映射和相互转变的可能性,它们的相互映射和相互转变将使关系数据库和面向对象数据库之间的转变成为可能。     

Choice of the primary analysis in longitudinal clinical trials

Missing data, and the bias they can cause, are an almost ever‐present concern in clinical trials. The last observation carried forward (LOCF) approach has been frequently utilized to handle missing data in clinical trials, and is often specified in conjunction with analysis of variance (LOCF ANOVA) for the primary analysis. Considerable advances in statistical methodology, and in our ability to implement these methods, have been made in recent years. Likelihood‐based, mixed‐effects model approaches implemented under the missing at random (MAR) framework are now easy to implement, and are commonly used to analyse clinical trial data. Furthermore, such approaches are more robust to the biases from missing data, and provide better control of Type I and Type II errors than LOCF ANOVA. Empirical research and analytic proof have demonstrated that the behaviour of LOCF is uncertain, and in many situations it has not been conservative. Using LOCF as a composite measure of safety, tolerability and efficacy can lead to erroneous conclusions regarding the effectiveness of a drug. This approach also violates the fundamental basis of statistics as it involves testing an outcome that is not a physical parameter of the population, but rather a quantity that can be influenced by investigator behaviour, trial design, etc. Practice should shift away from using LOCF ANOVA as the primary analysis and focus on likelihood‐based, mixed‐effects model approaches developed under the MAR framework, with missing not at random methods used to assess robustness of the primary analysis. Copyright © 2004 John Wiley & Sons, Ltd.