搞准规模以下工业抽样调查样本单位数据的思考

样本单位数据的准确性是规模以下工业抽样调查成功与否的关键环节之一。本文拟结合余杭实际,对如何搞准规模以下工业样本单位数据作一剖析和思考。一、搞准样本单位数据的意义1·是检验规模以下工业抽样调查成功与否的重要标准之一。规模以下抽样调查工作三个环节是环环相扣的,     

县级规模以下工业的抽样调查的初步体会

为全面、准确地反映年产品销售收入５００万元以下非国有工业企业和个体工业（以下简称规模以下工业）生产经营和总量情况 ,余杭市统计局从去年三月下旬起 ,在全市范围内开展了以市为总体推算单位的规模以下工业抽样调查工作。调查结果较客观地反映了全市规模以下工业发展现状 ,达到了预期目的。规模以下工业抽样调查涉及面广、难度大、要求高 ,经过半年来的实践摸索 ,笔者认为县一级搞好这项工作必须把握以下几点。前提———统一思想认识开展规模以下工业抽样调查是国家工业统计方法制度改革的核心内容和重大举措 ,是提高统计数据质量的有…     

浙江省规模以下工业抽样调查数据搜集方法研究

我国现行的工业统计根据企业的经济类型和规模大小采用两种不同的调查方法：年产品销售收入500万元以上的非国有工业企业和全部国有企业采用全面统计的方法,年产品销售收入500万元以下的非国有工业企业和个体工业经营户（以下简称规模以下工业）采用抽样调查的方法.虽然经过近十年的改革与实践,规模以下工业抽样调查方案不断完善、科学,调查和推算结果的质量不断提高,但在调查过程中,数据搜集仍是困扰数据质量提高的一大难题.本文从规模以下工业抽样调查样本单位的特征和现行数据搜集方法存在的问题入手,探讨规模以下工业抽样调查数据搜集应遵循的原则和注意的事项,为搜集较为准确的原始数据提供参考.     

县（市）规模以下工业抽样调查的问题与思考

县(市)规模以下工业抽样调查,经过理论上的努力探索,实践中的认真试点,取得了较好的经验。2002年县(市)规模以下工业总产值已由全面报表向抽样调查全面过渡,规模以下工业总产值采用抽样调查,科学推算,是统计方法制度改革的飞跃。但县(市)统计在贯彻执行这个方法制度的实践中又遇到了一些亟待解决的问题。     

开展工业抽样调查试点 推进统计汇总方式改革

1999年，围家统计局开对个部国有工业企业和年销售收入500万元及以上非国有工业企业实行超级汇总的基础上，将规模以下工业抽样调查作为进一步扩大抽样调查技术应用范围的突破口，争取用三年时间，逐步使规模以下工业抽样调查数据取代全面统计数据。无锡市作为江苏省工业抽样调查试点市，为贯彻执行国家、省统计工作会议精神，勤于实践、勇于探索，于1999年先后两次组织开展以市为总体的规模以下工业抽样调查试点工作，为今后抽样调查技术的推广应用积累了经验。对数量庞大，内部结构复杂，』总体单位分布分散的总体，如规模以下工业内容，…     

积极探索 精心组织 努力搞好规模以下工业抽样调查工作——李积文同志在全省规模以下工业抽样调查工作会议上的讲话

这次工作会议的主要任务是，总结2004年的规模以下工业抽样调查工作，经验交流；表彰先进；布置2004年全省规模以下工业抽样调查年报和2005年定期报表工作。下面，我讲几点意见：     

动态

运城地区规模以下工业企业抽样调查会议召开根据全省企业调查会议安排，3月16日，运城地区规模以下工业企业抽样调查会议在华夏大酒店召开。各县（市）统计局分管工业的副局长和企业调查员参加了会议。会议由企调队综合科科长黄云久同志主持，企调队专职副队长程胜喜同志到会并做了重要讲话。会议首先由企调队产业调查科副科长陈家业同志传达全省规模以下工业企业抽样调查会议精神，布置全区规模以下工业企业抽样调查任务。这次全区个体经营工业样本单独调查部分共涉及1998年营业收入1000万元以上的28个行政村，抽样调查44个行政村、乡及乡…     

对规模以下工业抽样调查的几点思考

一、规模以下工业抽样调查中值得关注的问题 1、统计基础工作依然薄弱.作为规模以下工业抽样调查的样本单位,无论是个体工业户还是小型工业企业,由于其规模小,财务制度不健全,财务统计人员配备先天不足,统计原始记录、统计台账的设置及小型工业企业统计单位登记工作远远落后于规模以上工业企业,基础工作的松浮,致使一些样本数据数出无据.     

对规模以下工业抽样调查的几点建议

一、规模以下工业抽样调查中值得关注的问题1、统计基础工作依然薄弱。作为规模以下工业抽样调查的样本单位,无论是个体工业户还是小型工业企业,由于其规模小,财务制度不健全,财务统计人员配备先天不足,统计原始记录、统计台账的设置及小型工业企业统计单位登记工作远远落后于规模以上工业企业,基础工作的松浮,致使一些样本数据数出无据。     

规模以下工业抽样调查项目的定位

抽样调查项目定位背景对统计调查项目的定位,是一个非常重要的问题。人们往往通过调查项目所要达到的目的,来评价这个调查项目。比如规模以下工业抽样调查统计项目就是如此。20世纪90年代中后期,市场经济的迅速发展,使全面调查报表制度无法适应小企业数量众多的、乡镇工业统计数据质量不高的局面,1996年国家统计局开始试点规模以下工业抽样调查,1998年确定这项调查制度,调查的目的定格为:为满足GDP核算总量提供基础数据。2005年规模以下工业抽样报表制度中这样描述调查的目的:反映规模以下工业的基本总量,为国民经济核算提供基础数据。规…     

Exactly optimal sampling designs for processes with a product covariance structure

The author considers the problem of finding exactly optimal sampling designs for estimating a second‐order, centered random process on the basis of finitely many observations. The value of the process at an unsampled point is estimated by the best linear unbiased estimator. A weighted integrated mean squared error or the maximum mean squared error is used to measure the performance of the estimator. The author presents a set of necessary and sufficient conditions for a design to be exactly optimal for processes with a product covariance structure. Expansions of these conditions lead to conditions for asymptotic optimality.     

Some results on maximum likelihood estimators of parameters of exponential distribution under type I progressive censoring with changing failure rates

In this paper the study of relative bias (RB), exact variance and mean square error (MSE) of the maximum likelihood estimators of the exponential distribution under type I progressive censoring with changing failure rates is considered. A minimum mean square error (MMSE) estimator for the parameter at each stage is proposed. The numerical evalution of their relative performance is made for selected values of n and p. Further results concerning group-censoring, total expected waiting time and optimal spacings of the times of censoring are derived and results obtained by Kendell and Anderson (1971) are deduced as special cases.     

Comparison of methods of estimation of parameters of the weibull distribution

The generalised least squares, maximum likelihood, Bain-Antle 1 and 2, and two mixed methods of estimating the parameters of the two-parameter Weibull distribution are compared. The comparison is made using (a) the observed relative efficiency of parameter estimates and (b) themean squared relative error in estimated quantiles, to summarize the results of 1000 simulated samples of sizes 10 and 25. The results are that: generalised least squares is the best method of estimating the shape parameter ß the best method of estimating the scale parameter a depends onthe size of ß for quantile estimation maximum likelihood is best Bain-Antle 2 is uniformly the worst of the methods.     

On the simultaneous effects of model misspecification and errors in variables

Model misspecification and noisy covariate measurements are two common sources of inference bias. There is considerable literature on the consequences of each problem in isolation. In this paper, however, the author investigates their combined effects. He shows that in the context of linear models, the large‐sample error in estimating the regression function may be partitioned in two terms quantifying the impact of these sources of bias. This decomposition reveals trade‐offs between the two biases in question in a number of scenarios. After presenting a finite‐sample version of the decomposition, the author studies the relative impacts of model misspecification, covariate imprecision, and sampling variability, with reference to the detectability of the model misspecification via diagnostic plots.     

On exact computation of minimum sample size for restricted estimation of a binomial parameter

The problem of determining minimum sample size for the estimation of a binomial parameter with prescribed margin of error and confidence level is considered. It is assumed that available auxiliary information allows to restrict the parameter space to some interval whose left boundary is above zero. A range-preserving estimator resulting from the conditional maximization of the likelihood function is considered. A method for exact computation of minimum sample size controlling for the relative error is proposed. Several tables of minimum sample sizes for typical situations are also presented. The range-preserving estimator achieves the same precision and confidence level as the unrestricted maximum likelihood estimator but with a smaller sample.     

Extrapolation designs with constraints

The author considers (asymptotically) minimax extrapolation designs for an approximately multiple linear model with the model contaminant f being restricted only by its L₂ norm. He splits the integrated mean squared prediction error (IMSPE) of the fitted value over the extrapolation space into two parts, namely the integrated prediction variance (IPV) and the integrated prediction bias (IPB). For a spherical design space and an annular extrapolation space, he constructs the design that minimizes the maximum value, over f, of IPB subject to bounding IPV. He also constructs the design that minimizes IPV subject to bounding the maximum IPB.     

Loss functions for estimation of extrema with an application to disease mapping

It is often of interest to find the maximum or near maxima among a set of vector‐valued parameters in a statistical model; in the case of disease mapping, for example, these correspond to relative‐risk “hotspots” where public‐health intervention may be needed. The general problem is one of estimating nonlinear functions of the ensemble of relative risks, but biased estimates result if posterior means are simply substituted into these nonlinear functions. The authors obtain better estimates of extrema from a new, weighted ranks squared error loss function. The derivation of these Bayes estimators assumes a hidden‐Markov random‐field model for relative risks, and their behaviour is illustrated with real and simulated data.     

Estimating the Burr XII Parameters in Constant-Stress Partially Accelerated Life Tests Under Multiple Censored Data

In this article, we present the performance of the maximum likelihood estimates of the Burr XII parameters for constant-stress partially accelerated life tests under multiple censored data. Two maximum likelihood estimation methods are considered. One method is based on observed-data likelihood function and the maximum likelihood estimates are obtained by using the quasi-Newton algorithm. The other method is based on complete-data likelihood function and the maximum likelihood estimates are derived by using the expectation-maximization (EM) algorithm. The variance–covariance matrices are derived to construct the confidence intervals of the parameters. The performance of these two algorithms is compared with each other by a simulation study. The simulation results show that the maximum likelihood estimation via the EM algorithm outperforms the quasi-Newton algorithm in terms of the absolute relative bias, the bias, the root mean square error and the coverage rate. Finally, a numerical example is given to illustrate the performance of the proposed methods.     

On the application of extended quasi‐likelihood to the clustered data case

The author describes the relationship between the extended generalized estimating equations (EGEEs) of Hall & Severini (1998) and various similar methods. He proposes a true extended quasi‐likelihood approach for the clustered data case and explores restricted maximum likelihood‐like versions of the EGEE and extended quasi‐likelihood estimating equations. He also presents simulation results comparing the various estimators in terms of mean squared error of estimation based on three moderate sample size, discrete data situations.     

On generalized least squares estimation of the weibull distribution

A simple estimation procedure, based on the generalized least squares method, for the parameters of the Weibull distribution is described and investigated. Through a simulation study, this estimation technique is compared with maximum likelihood estimation, ordinary least squares estimation, and Menon's estimation procedure; this comparison is based on observed relative efficiencies (that is, the ratio of the Cramer-Rao lower bound to the observed mean squared error). Simulation results are presented for samples of size 25. Among the estimators considered in this simulation study, the generalized least squares estimator was found to be the "best" estimator for the shape parameter and a close competitor to the maximum likelihood estimator of the scale parameter.