居民生活碳排放抽样调查方案设计:以青海省为例

调查方案设计是居民生活碳排放调查及数据分析的基础和保证.以大量文献和作者在居民生活碳排放调查中的实践经验为基础,立足调查方案的科学性和实践操作的可行性,以青海省为例,从居民生活碳排放调查的统计量选取、抽样框的构建、抽样方法的选择和样本量的确定、调查方案实施的详细步骤4方面综述性归纳了一套居民生活碳排放调查方案设计的方法,以期为居民生活碳排放研究提供参考.     

关于我国2010年人口普查事后质量检查样本量测算的建议

同以往历次进行的人口普查一样,我国在2010年第六次人口普查后也将进行事后质量检查。样本量估计是抽取事后质量检查样本之前必须要做的一项工作。本人建议,用2000年人口普查事后质量检查样本资料估计其抽样方案的设计效应,然后用这个设计效应估计2010年在既定精度要求下所需要的样本量。     

基于辅助信息的分层排序集抽样及调查费用分析

辅助信息在改进和完善抽样设计、提高抽样估计精度和节省抽样费用等方面具有重要作用,鉴于此,基于分层排序集样本建立了总体均值的比率估计量,同时考虑估计精度和调查费用两个方面,证明了抽样方案的优良性.最后,通过实例进一步分析,结果表明,在给定的估计精度下,分层排序集抽样方法可以有效降低抽样调查费用.     

基于辅助信息的分层排序集抽样及调查费用分析北大核心

辅助信息在改进和完善抽样设计、提高抽样估计精度和节省抽样费用等方面具有重要作用,鉴于此,基于分层排序集样本建立了总体均值的比率估计量,同时考虑估计精度和调查费用两个方面,证明了抽样方案的优良性.最后,通过实例进一步分析,结果表明,在给定的估计精度下,分层排序集抽样方法可以有效降低抽样调查费用.     

多目标调查的MPPS平衡抽样方法研究

平衡抽样是广受关注的利用辅助信息改善样本结构的抽样方法.MPPS抽样在多目标调查中采用的与多变量规模成比例的入样概率,可以在平衡抽样中精确满足.基于MPPS抽样和平衡抽样的性质,文章提出多目标调查下的MPPS平衡抽样,该方法主要思想是将多个调查变量的辅助信息在确定入样概率和随机抽样过程中同时使用,提高HT估计量精度.模...     

定时截尾下指数分布产品可靠性抽样检验方案

本文给出了制订定时截尾下指数分布产品可靠性抽样检验方案的统计方法.检验统计量是平均寿命倒数的极大似然估计.提出了一种选择截尾时间的方法.利用分布分位数的Cornish-Fisher展开近似地确定了样本量和接收常数.模拟结果表明,本文给出的方法是可行的.     

一种满足有效样本量固定的放回抽样方法

相比不放回抽样,放回抽样的实施比较简单,操作性强,但缺点在于单元可能被重复抽到,抽出的有效样本量小于等于样本量,不是固定的。本文应用逆抽样的原理,设计了一种放回抽样方法,满足有效样本量固定,并且估计量的性质优良。     

基于处理效应模型的评估方法改进及应用研究

处理效应模型广泛应用于教育、医学和公共政策研究中,模型估计结果依赖于处理对象的选择,特别是当样本量较小时,估计结论缺乏稳健性.在这种情形下,排序集抽样是一种经济有效的方法.基于排序集样本研究了改进的平均处理效应估计模型,对改进后估计量的无偏性和渐进性进行了证明,建立了估计量精度与回归系数估计精度之间的关系.结果 表明,...     

人体测量抽样方案目标量的估计及样本量的确定

本文综述了与人体测量抽样方案有关的一些问题,提出了一种总体分位数估计量X_p的(相对)精度的定义,给出了构造X_p的方法,讨论了X_p的精度与样本量的关系以及为制定服装号型系列标准有关的一些问题。     

估计敏感特征比例和敏感指标均值的问卷调查技术

提出了一种敏感性问题问卷调在技术.在相同的样本量下,提出的问卷调查技术可以获得调查者所关心的两项敏感指标(敏感比例π_x和敏感指标均值μ_x).此外,还定义了关于敏感指标的极大似然估计形式,给出一种问卷调查装置对个体隐私保护的度量方法.利用Monte Carlo方法模拟计算了在简单随机有放回抽样设计方案下估计量的精度和装置的保护度.     

Random sampling within the framework of a multivariate principal-agent approach

In this paper, we analyze a specific class of principal-agent models which seems to be sufficiently general to cover applications in environmental economics with upstream-downstream problems as an example. In our basic model, the observation outcome is ann-dimensional random vectorx and only the first and second moments ofx are common knowledge. We study the effects of random sampling in the presence of costly signals. For this purpose, we assume that the principal and the agent use a simple statistical procedure, i.e. their contract will be based on the mean of a random sample with sampling costs dependent on the sample size. It is shown that there exists an optimal sample size. We investigate the relationship between the optimal sample size, the marginal sampling costs, and the agent's risk aversion.     

The exact bootstrap method shown on the example of the mean and variance estimation

The bootstrap method is based on resampling of the original randomsample drawn from a population with an unknown distribution. In the article it was shown that because of the progress in computer technology resampling is actually unnecessary if the sample size is not too large. It is possible to automatically generate all possible resamples and calculate all realizations of the required statistic. The obtained distribution can be used in point or interval estimation of population parameters or in testing hypotheses. We should stress that in the exact bootstrap method the entire space of resamples is used and therefore there is no additional bias which results from resampling. The method was used to estimate mean and variance. The comparison of the obtained distributions with the limit distributions confirmed the accuracy of the exact bootstrap method. In order to compare the exact bootstrap method with the basic method (with random sampling) probability that 1,000 resamples would allow for estimating a parameter with a given accuracy was calculated. There is little chance of obtaining the desired accuracy, which is an argument supporting the use of the exact method. Random sampling may be interpreted as discretization of a continuous variable.     

二阶段整群抽样的样本大小估计公式及其在医疗费用调查中的应用

本文利用哥西不等式原理及求极小值方法 ,在限定抽样误差的大小使调查花费最小及限定调查花费的大小使抽样误差最小两种情况下 ,对常用的二阶段整群抽样 ,推导出其最优样本大小计算公式 ,在中国铁路职工医疗费用的调查中取得了成功的应用。     

Sequential Fixed-Width Confidence Interval for the Product of Two Means

For the product of two population means, the problem of constructing a fixed-width confidence interval with preassigned coverage probability is considered. It is shown that the optimal sample sizes which minimize the total sample size and at the same time guarantee a fixed-width confidence interval of desired coverage depend on the unknown parameters. In order to overcome this, a fully sequential procedure consisting of a sampling scheme and a stopping rule are proposed. It is then shown that the sequential confidence interval is asymptotically consistent and the stopping rule is asymptotically efficient, as the width goes to zero. Furthermore, a second order result for the difference between the expected stopping time and the (total) optimal fixed sample size is established. The theoretical results are supported by appropriate simulations.     

混凝土构件检测样本容量的确定方法

混凝土构件检测时,一般采用百分比抽样的方式.以回弹法中对混凝土强度进行检测时采用百分比抽样的抽样方式为例,从绝对误差限和相对误差限的角度分析了不同构件总量均采用此方法抽取样本的不合理性,并提出在不同混凝土强度等级、不同构件总数的情况下,通过控制一定的误差限来确定样本数量的方法.通过理论和实例分析,提出在抽样过程中,采用分层抽样技术对检测构件进行合理分层,降低总体方差,可减少样本数量.这种方法也适用于其它检测问题的样本容量的确定.     

A biobjective method for sample allocation in stratified sampling

The two main and contradicting criteria guiding sampling design are accuracy of estimators and sampling costs. In stratified random sampling, the sample size must be allocated to strata in order to optimize both objectives.     

Optimal subsampling for large-scale quantile regression

To deal with massive data sets, subsampling is known as an effective method which can significantly reduce computational costs in estimating model parameters. In this article, an efficient subsampling method is developed for large-scale quantile regression via Poisson sampling framework, which can solve the memory constraint problem imposed by big data. Under some mild conditions, large sample properties for the estimator involving the weak and strong consistencies, and asymptotic normality are established. Furthermore, the optimal subsampling probabilities are derived according to the A-optimality criterion. It is shown that the estimator based on the optimal subsampling asymptotically achieves a smaller variance than that by the uniform random subsampling. The proposed method is illustrated and evaluated through numerical analyses on both simulated and real data sets.     

Improved attribute acceptance sampling plans in the presence of misclassification error

This paper considers an attribute acceptance sampling problem in which inspection errors can occur. Unlike many common situations, the source of the inspection errors is the uncertainty associated with statistical sampling. Consider a lot that consists of N containers, with each container including a large number of units. It is desired to sample some of the containers and inspect a sample of units from these selected containers to determine proper disposition of the entire lot. Results presented in the paper demonstrate significant shortcomings in traditional sampling plans when applied in this context. Alternative sampling plans designed to address the risk of statistical classification error are presented. These plans estimate the rate of classification errors and set plan parameters to reduce the potential impact of such errors. Results are provided comparing traditional plans with the proposed alternatives. Limitations of the new plans are also discussed.     

Sequential and non-sequential acceptance sampling plans for autocorrelated processes using ARMA(p,q) models

This paper presents variable acceptance sampling plans based on the assumption that consecutive observations on a quality characteristic(X) are autocorrelated and are governed by a stationary autoregressive moving average (ARMA) process. The sampling plans are obtained under the assumption that an adequate ARMA model can be identified based on historical data from the process. Two types of acceptance sampling plans are presented: (1) Non-sequential acceptance sampling: In this case historical data is available based on which an ARMA model is identified. Parameter estimates are used to determine the action limit (k) and the sample size(n). A decision regarding acceptance of a process is made after a complete sample of size n is selected. (2) Sequential acceptance sampling: Here too historical data is available based on which an ARMA model is identified. A decision regarding whether or not to accept a process is made after each individual sample observation becomes available. The concept of Sequential Probability Ratio Test (SPRT) is used to derive the sampling plans. Simulation studies are used to assess the effect of uncertainties in parameter estimates and the effect of model misidentification (based on historical data) on sample size for the sampling plans. Macros for computing the required sample size using both methods based on several ARMA models can be found on the author’s web page .