中国、新加坡通关模式比较及启示

随着我国改革开放逐步深入和外向型经济的快速发展,口岸通关的提速和便捷已成一个十分重要的投资条件。而我国传统的通关模式已不能适应快速发展的经济和对外贸易的需要。因此,就新加坡与我国通关模式进行介绍分析比较,并提出我国实施新型通关模式的建议。     

CRH动车转向架检测输送线的研制

简要介绍了应用于上海铁路局上海虹桥站动车段、武汉铁路局客运专线动车组检修基地上使用的CRH动车转向架检测输送线的组成、结构特点、主要技术参数及使用情况。     

实施闭环检验,有效降低质量风险

采取抽样检验的方式来检验产品存在一定的质量风险。实施闭环检验的方式才能在检验的时候采取措施杜绝漏检及时调整检验的方案。质量检验与质量管理相互合作,才可以有效的降低在检验上出现质量的风险。     

数控车床测头系统的开发

随着现代制造技术的不断发展,我国数控车床测头系统的开发也得到发展。本文分析了测头系统的作用、组成及工作原理,以在数控车床上自动在线测量孔直径为例,介绍了测头系统测量程序的编制方法,使数控机床的操作变得更加简单、便捷、精确、高效,同时为其他相关程序的编制提供参考。     

检学合作的实践与思考

本文作者通过泉州检验检疫局与泉州医学高等专科学校,建立检学合作的探索与实践,浅谈检学教学合作的意义,推动检学合作的几点思考与建议,与大家探讨。以期通过深化检学合作,助力科技质检建设。     

A Stochastic Frontier Analysis of Output Level and Growth in Poland and Western Economies

This paper uses Bayesian stochastic frontier methods to measure the productivity gap between Poland and Western countries that existed before the beginning of the main Polish economic reform. Using data for 20 Western economies, Poland and Yugoslavia (1980–1990) we estimate a translog stochastic frontier and make inference about individual efficiencies. Following the methodology proposed in our earlier work, we also decompose output growth into technical, efficiency and input changes and examine patterns of growth in the period under consideration. This revised version was published online in July 2006 with corrections to the Cover Date.     

Mean square error estimation in multi-stage sampling

Summary: Suppose for a homogeneous linear unbiased function of the sampled first stage unit (fsu)-values taken as an estimator of a survey population total, the sampling variance is expressed as a homogeneous quadratic function of the fsu-values. When the fsu-values are not ascertainable but unbiased estimators for them are separately available through sampling in later stages and substituted into the estimator, Raj (1968) gave a simple variance estimator formula for this multi-stage estimator of the population total. He requires that the variances of the estimated fsu-values in sampling at later stages and their unbiased estimators are available in certain `simple forms'. For the same set-up Rao (1975) derived an alternative variance estimator when the later stage sampling variances have more ‘complex forms’. Here we pursue with Raj's (1968) simple forms to derive a few alternative variance and mean square error estimators when the condition of homogeneity or unbiasedness in the original estimator of the total is relaxed and the variance of the original estimator is not expressed as a quadratic form.  We illustrate a particular three-stage sampling strategy and present a simulation-based numerical exercise showing the relative efficacies of two alternative variance estimators. Received: 19 February 1999     

Full Likelihood Inference in Normal-Gamma Stochastic Frontier Models

The paper takes up inference in the stochastic frontier model with gamma distributed inefficiency terms, without restricting the gamma distribution to known integer values of its shape parameter (the Erlang form). The paper shows that Gibbs sampling with data augmentation can be used in a computationally efficient way to explore the posterior distribution of the model and conduct inference regarding parameters as well as functions of interest related to technical inefficiency.     

简析ISO9001标准中检验和试验记录控制的主要内容

扼要地将ＩＳＯ９００１标准中对检验和试验记录控制要求作以介绍,特别提出检验记录与不合格品控制程序的接口以及授权放行的层次,强调印章管理的重要性和方法。     

Modified efficient importance sampling for partially non‐Gaussian state space models

The construction of an importance density for partially non‐Gaussian state space models is crucial when simulation methods are used for likelihood evaluation, signal extraction, and forecasting. The method of efficient importance sampling is successful in this respect, but we show that it can be implemented in a computationally more efficient manner using standard Kalman filter and smoothing methods. Efficient importance sampling is generally applicable for a wide range of models, but it is typically a custom‐built procedure. For the class of partially non‐Gaussian state space models, we present a general method for efficient importance sampling. Our novel method makes the efficient importance sampling methodology more accessible because it does not require the computation of a (possibly) complicated density kernel that needs to be tracked for each time period. The new method is illustrated for a stochastic volatility model with a Student's t distribution.