关于捕获再捕获抽样的置信区间

捕获再捕获抽样是一种应用广泛的抽样方法。运用随机模拟,研究捕获再捕获抽样的三种估计量的均值、方差、偏度、峰度、利用近似正态分布构造的置信区间的统计性质。改进了估计量的样本方差计算公式,使得利用近似正态分布构造的置信区间更优。     

捕获再捕获抽样估计量的模拟研究

目前,捕获再捕获抽样已广泛应用于生物科学、社会科学、生命科学和医学的调查研究中。为此,对捕获再捕获抽样的最常用的Lincoln-Petersen估计量和Chapman估计量进行模拟比较,提出新的有偏估计量,其偏差介于上述两个估计量之间。理论和模拟结果显示：新估计量有时优于其它两个估计量;在实际应用中,当上述两个估计量较难取舍时,新估计量可以作为一个更好的选择。     

部份样本轮换抽样下的比估计

部份样本轮换抽样下的比估计辽宁大学国民经济管理学马树才，韩云虹在实际抽样调查中，当对总体的前期抽取一定容量的样本个体进行调查后，在对总体现期抽样调查时，人们常常将前期抽样的样本个体部分采取轮换的方式进行，以期望提高对总体的现期估计的精度。那么它对总体...     

捕获——再捕获抽样在生物统计中的应用

0引言在生态研究和野生动物研究中,估计野生动物数量是研究者通常需要做的工作。1896年,Peterson首先提出了捕获—再捕获抽样方法,并将该方法用于野生动物的数量研究中。从此,对它的研究不断深入,应用领域也在不断扩展。捕获—再捕获抽样的基本思想方法是从总体中抽取一个样本,     

多阶段抽样比率估计

目前 ,单阶段、两阶段的抽样理论较丰富 (见文献 [1])。三阶段以上的抽样理论不完善。在这篇文章中 ,我们讨论多阶段抽样比率估计 ,给出了样本估计抽样均方误差及其无偏估计的公式。首先将总体Ｍ个单位分为Ｒ个一级组 ,第ｉ1(1≤ｉ1≤Ｒ)个一级组包含Ｒｉ1个二级组 ,第ｉ1一级组中第ｉ2 (1≤ｉ2 ≤Ｒｉ1)个二级组包含Ｒｉ1,ｉ2 个三级组 ,… ,直到前ｎ - 2级组分别是第ｉ1,ｉ2 ,… ,ｉｎ -2 个的ｎ- 2级组包含Ｒｉ1,… ,ｉｎ-2 个ｎ - 1级组。第ｉｎ -1个 (1≤ｉｎ -1≤Ｒｉ1,… ,ｉｎ -2 )ｎ - 1级组包含Ｒｉ1,… ,ｉｎ -1个总体单位。则Ｍ…     

多目标抽样的区间估计方法

实践中经常会碰到一些多目标抽样的问题,由于总体单位在各个目标上的差异往往很大,给抽样调查的设计、目标量的估计、样本容量的确定以及抽样误差的控制等方面带来了困难,因而如何兼顾各个目标的要求是多目标抽样研究的难题之一。笔者试提出一种多目标抽样的区间估计方法,希望能够从一个总体的角度对总体参数的置信区间进行测量,从而避免各个目标     

对多目标抽样的再认识

文章针对多目标抽样的理论与实践中存在的问题展开讨论,阐述了多目标抽样调查的含义,提出了为多目标抽样所需要解决的一系列的问题,并加以分类。     

基尼系数的抽样估计

The paper discusses the properties of statistics and precision of estimation when the sample statistics are used to estimate Gini Coefficient.     

多阶段抽样第一阶段样本──含量问题初探

多阶段抽样第一阶段样本──含量问题初探哈尔滨城市社会经济调查队那宪义本文以哈尔滨市住户调查实践为背景，通过对多阶段抽样方差，变异系数的分析得出的结论：多阶段抽样的关键，在于抽好第一阶段样本，尽可能便第一阶段样本含量大一些，以减少抽样方差，保证样本的代...     

轮换样本抽样下的回归估计

轮换样本抽样是一种较新的抽样方法，它可克服固定样本抽样的缺点，而且在一定程度上可节省调查时间和经费。对轮换样本进行回归估计可充分利用前期样本的资料，通过提高前后期样本的相关系数和控制样本的轮换单位数来减小由样本推断总体的估计方差，轮换样本单位数少于原样本单位数的一半时，轮换样本单位数越多，估计精度越高，当轮换样本单位数是原样本单位数的一半时估计精度最高     

Estimation of Mean of Sensitive Characteristics for Successive Sampling

In this article, new estimators for estimating the population mean of a sensitive variable using the concept of successive sampling over two occasions are proposed. The unbiasedness and the variance properties of the proposed estimators are investigated analytically as well as numerically.     

Survey sampling in graphs

The Horvitz-Thompson estimation theory is applied to snowball sampling and some other sampling procedures using a known or unknown graph structure in the survey population. In particular, simple graph-parameter estimators and variance estimators are obtained which are based on various kinds of partial information about the graph.     

Efficient Use of Two Decks of Cards in Randomized Response Sampling

This paper extends the work of Russell (1976). Proof Is given that, for many parameter sets, all O:XB designs belonging to the set are connected. It is shown how an (M,S)-optinal design nay be selected from the M-optimal designs of a given parameter set. The efficiencies of these (M,S)-optimal designs are displayed, and it is concluded that the (M,S)-optimality criterion is useful for selecting designs of high efficiency.     

A New Sampling Design for Systematic Sampling

A sampling design called “Modified Systematic Sampling (MSS)” is proposed. In this design each unit has an equal probability of selection. Moreover, it works for both situations: N = nk or N ≠ nk. Consequently, the Linear Systematic Sampling (LSS) and Circular Systematic Sampling (CSS) become special cases of the proposed MSS design.     

A List Sequential Sampling Method Suitable for Real-Time Sampling

Abstract.  A flexible list sequential π ps sampling method is introduced and studied. It can reproduce any given sampling design without replacement, of fixed or random sample size. The method is a splitting method and uses successive updating of inclusion probabilities. The main advantage of the method is in real-time sampling situations where it can be used as a powerful alternative to Bernoulli and Poisson sampling and can give any desired second-order inclusion probabilities and thus considerably reduce the variability of the sample size.     

The Horvitz-Thompson Theorem as a Unifying Perspective for Probability Sampling: With Examples from Natural Resource Sampling

Courses in sampling often lack a coherent structure because many related sampling designs, estimators, variances, and variance estimators are presented as separate cases. The Horvitz-Thompson theorem offers a needed integrating perspective for teaching the methods and fundamental concepts of probability sampling. Development of basic concepts in sampling via this approach provides the student with tools to solve more complicated problems, and helps to avoid some common stumbling blocks of beginning students. Examples from natural resource sampling are provided to illustrate applications and insight gained from this approach.     

A Conditionally Unbiased Estimator for the Equal-Catchability Model

ABSTRACT

In this article we reconsider an estimator of population size previously advocated for use when sampling from a population subdivided into different types. We show that it may be usefully adopted in the simple equal-catchability model used in mark-recapture. Unlike the commonly used maximum likelihood estimator, this conditionally unbiased estimator is always finite-valued. Except in situations in which the data contain little relevant information, its performance, in terms of bias and precision, is seen to be at least as good as that of the maximum likelihood estimator. Two estimators of the standard deviation of the conditionally unbiased estimator are considered.     

An Extension of Sampford's Method for Unequal Probability Sampling

Abstract. Sampford's unequal probability sampling method is extended to the case that the inclusion probabilities do not sum to an integer. In this case, the sampling outcome is left open for exactly one randomly chosen unit and that unit gets a new inclusion probability. Three applications are presented. Two of them challenge traditional sampling routines. The simple Pareto sampling design, which was introduced by Rosén in 1997, is also extended. The extended Pareto design is shown to be close to the extended Sampford design.     

L Ranked Set Sampling: A Generalization Procedure for Robust Visual Sampling

In this article, a robust ranked set sampling (LRSS) scheme for estimating population mean is introduced. The proposed method is a generalization for many types of ranked set sampling that introduced in the literature for estimating the population mean. It is shown that the LRSS method gives unbiased estimator for the population mean with minimum variance providing that the underlying distribution is symmetric. However, for skewed distributions a weighted mean is given, where the optimal weights is computed by using Shannon's entropy. The performance of the population mean estimator is discussed along with its properties. Monte Carlo comparisons for detecting outliers are made with the traditional simple random sample and the ranked set sampling for some distributions. The results indicate that the LRSS estimator is superior alternative to the existing methods.     

Markov Sampling

A discrete parameter stochastic process is observed at epochs of visits to a specified state in an independent two-state Markov chain. It is established that the family of finite dimensional distributions of the process derived in this way, referred to as Markov sampling, uniquely determines the stochastic structure of the original process. Using this identifiability, it is shown that if the derived process is Markov, then the original process is also Markov and if the derived process is strictly stationary then so is the original.