悬浮液进样平台石墨炉原子吸收光谱测定土壤中痕量镉

采用悬浮液进样平台石墨炉原子吸收光谱测定土壤中痕量镉,对测定条件、悬浮液稳定均匀性、基本改进剂、介质酸度及校正方法均进行了实验选择．本法的精密度为6.3%,检出出限为1.7*10-2g ,回收率为94%-107%     

结构可靠性分析中蒙特卡洛模拟的应用

本文讨论了蒙特卡洛模拟在结构可靠性分析中的应用 ,并给出了直接抽样法中失效概率的计算公式和置信区间     

Designing of a variables two-plan system by minimizing the average sample number

This article proposes a variables two-plan sampling system called tightened-normal-tightened (TNT) sampling inspection scheme where the quality characteristic follows a normal distribution or a lognormal distribution and has an upper or a lower specification limit. The TNT variables sampling inspection scheme will be useful when testing is costly and destructive. The advantages of the variables TNT scheme over variables single and double sampling plans and attributes TNT scheme are discussed. Tables are also constructed for the selection of parameters of known and unknown standard deviation variables TNT schemes for a given acceptable quality level (AQL) and limiting quality level (LQL). The problem is formulated as a nonlinear programming where the objective function to be minimized is the average sample number and the constraints are related to lot acceptance probabilities at AQL and LQL under the operating characteristic curve.     

Double shrink methodologies to determine the sample size via covariance structures

We consider fixed-size estimation for a linear function of mean vectors from _πi:_Np(_μi,_Σi)${\pi }_i:{\mathrm{N}}_p({\mathit{\mu }}_i,{\mathbf{\Sigma }}_i)$, i=1,…,k$i=1,\dots ,k$, when every _Σi${\mathbf{\Sigma }}_i$ has some structure. The goal of inference is to construct a fixed-span confidence region with required accuracy. We find a sample size for each _πi${\pi }_i$ with the help of the ‘double shrink methodology’, that is introduced by this paper, via covariance structures of _Σi${\mathbf{\Sigma }}_i$, i=1,…,k$i=1,\dots ,k$. We estimate the sample size in a two-stage sampling and give a fixed-span confidence region that has the coverage probability approximately second-order consistent with the required accuracy. Some simulations are carried out to see moderate sample size performances of the proposed methodologies.     

A two-phase sampling strategy for large-scale forest carbon budgets

The estimation of forest carbon sources and sinks is an important issue in forest inventories. For monitoring forest carbon budgets, a two-phase strategy is proposed. In the first phase, N points are thrown onto the study region by means of unaligned systematic sampling and then classified as forest or non-forest on the basis of aerial information. In the second phase, a sample of n points of those classified as forest is achieved by means of a sampling scheme with selection probabilities proportional to the sizes of an auxiliary variable. Subsequently, circular plots of adequate size are centred in the n selected points and visited in order to quantify the carbon amount within. Unbiased estimators of the forest area size as well as of the overall amount of carbon in that area and a conservative estimator of their variance–covariance matrix are derived. The strategy is applied to estimate carbon budgets in the forests of Trentino (North Italy).     

On the existence and uniqueness of the NPMLE in biased sampling models

Identifiability, existence and uniqueness of the non-parametric maximum likelihood estimator (NPMLE) in biased sampling models are thoroughly investigated. The conditions for existence and uniqueness are considered from a different perspective and proofs are simplified. An extension to semi-parametric models is provided.     

Two-stage informative cluster sampling—estimation and prediction with applications for small-area models

This paper considers the effects of informative two-stage cluster sampling on estimation and prediction. The aims of this article are twofold: first to estimate the parameters of the superpopulation model for two-stage cluster sampling from a finite population, when the sampling design for both stages is informative, using maximum likelihood estimation methods based on the sample-likelihood function; secondly to predict the finite population total and to predict the cluster-specific effects and the cluster totals for clusters in the sample and for clusters not in the sample. To achieve this we derive the sample and sample-complement distributions and the moments of the first and second stage measurements. Also we derive the conditional sample and conditional sample-complement distributions and the moments of the cluster-specific effects given the cluster measurements. It should be noted that classical design-based inference that consists of weighting the sample observations by the inverse of sample selection probabilities cannot be applied for the prediction of the cluster-specific effects for clusters not in the sample. Also we give an alternative justification of the Royall [1976. The linear least squares prediction approach to two-stage sampling. Journal of the American Statistical Association 71, 657–664] predictor of the finite population total under two-stage cluster population. Furthermore, small-area models are studied under informative sampling.     

When does an imperfect sampling frame produce more efficient estimators than a perfect frame?

The issue of when imperfect sampling frames can result in more efficient estimators of population totals than perfect frames is explored. Our analysis is based on an expression we call the difference score. We show how, when properly expanded it provides an illuminating basis for comparing a weighted estimator under an imperfect frame with that of a conventional estimator assuming the frame has been corrected. Specifically, the circumstances (i.e., population and frame characteristics) under which an imperfect frame results in estimates of population totals that are more precise than those from a perfect frame can in many cases be discerned by analytically examining the terms in the expansion of this difference score. In addition, a classification tree methodology was used to further explore circumstances under which imperfect frames result in more precise estimators. The results of this analytical study complement, strengthen, and in many cases explain those discovered in an earlier empirical investigation that lead to recommendations as to when to correct a frame or when to adjust for imperfection using a weighting methodology called the arc weight estimator.     

Gibbs抽样条件下瓦格纳法则的中国有效性研究

 本文在Gibbs抽样条件下,利用我国1952-2006年经济增长和财政支出的时间序列数据,对瓦格纳法则在中国有效性问题进行了实证检验,并具体考察了中国政府公共支出规模扩张与经济增长之间的平滑变参数特征。实证结果表明中国经济增长与政府公共支出增长之间的因果关系并不确定,两者在数量上的关系具有明显的阶段性特点,其参数在具体形态上呈平滑变参数特征。     

Estimating network properties from snowball sampled data

This article addresses the estimation of topological network parameters from data obtained with a snowball sampling design. An approximate expression for the probability of a vertex to be included in the sample is derived. Based on this sampling distribution, estimators for the mean degree, the degree correlation, and the clustering coefficient are proposed. The performance of these estimators and their sensitivity with respect to the response rate are validated through Monte Carlo simulations on several test networks. Our approach has no complex computational requirements and is straightforward to apply to real-world survey data. In a snowball sample design, each respondent is typically enquired only once. Different from the widely used estimator for Respondent-Driven Sampling (RDS), which assumes sampling with replacement, the proposed approach relies on sampling without replacement and is thus also applicable for large sample fractions. From the simulation experiments, we conclude that the estimation quality decreases with increasing variance of the network degree distribution. Yet, if the degree distribution is not to broad, our approach results in good estimates for the mean degree and the clustering coefficient, which, moreover, are almost independent from the response rate. The estimates for the degree correlation are of moderated quality.