随机化回答模型的探讨

随机化回答模型的探讨／赵俊康在社会调查中,打许多涉及被调查者隐私的问题,如储蓄、收入、吸毒、性关系等等,一般称为社会敏感问题。采用通常的方法对社会敏感问题进行调查,很难取得真实可靠的资料。为此,沃纳（ｗａｒｎｅｒ）于１９６５年设计出一种随机化回答模型...     

随机化回答技术的应用

在社会经济的调查中有时会涉及一些敏感性问题。例如,巾场工商管理部门要了解个体户漏税的比例有多大;计划生育部门要了解在已婚的育龄妇女中有多少人采取了避孕措施;公安部门要了解吸毒者人数的比例;学校要了解考试中学生作弊的情况等等。对于这类问题,容易引起被调查者的反感,他们或者不提供真实情况或者拒绝回答,从而使调查失败。一般而言,在调查中应尽量避免此类问题,以保证调查资料的质量,但如果研究的课题就是这类敏感问题,在调查中就无法回避。为了获得对这类问题总体比例进行估计的资料,就需要采用随机化回答技术,本文拟对此介绍一下随机化回答技术的应用。     

敏感性问题随机化回答模型的改进

敏感性问题随机化回答模型的改进孔圣元孟生旺ＡＢＳＴＲＡＣＴＴｈｅｐａｐｅｒｆｉｒｓｔｅｖａｌｕａｔｅｓｈｏｒｔａｇｅｏｆｅｘｉｓｔｉｎｇｒａｎｄｏｍｒｅｓｐｏｎｓｅｍｏｄｅｌｏｆｓｅｎｓｉ－ｔｉｖｅｐｒｏｂｌｅｍｓ,ａｎｄｔｈｅｎｓｅｔｕｐａｎｅｗｒ...     

整群抽样下的随机化回答模型

整群抽样下的随机化回答模型赵俊康随机化回答模型作为一种调查社会敏感问题的抽样方法,具有重要的现实意义,在我国社会经济统计中有着广泛的应用前景。但目前对这种抽样技术的研究仅限于简单随机抽样,从而使得这种抽样技术的推广应用受到了很大的限制。本文把随机化回...     

对敏感性问题调查之随机化回答技术的思考

抽样调查在人们生活、工作的方方面面中起着越发重要的作用 ,但数据的无回答现象降低了数据的质量 ,使调查的精度大打折扣。针对于此 ,本文介绍了在敏感性问题调查中使用频率颇高的沃纳模型和西蒙斯模型 ,阐述了随机化回答模型的基本原理 ,并比较了两种模型 ,指出了沃纳模型应用的局限性及西蒙斯模型在此基础上进行的改进。最后提出了在我国应用随机化回答技术应注意的几个问题     

分层抽样下的西蒙斯随机化回答模型

本文通过结合分层抽样技术和西蒙斯模型,提出了分层抽样下奈曼分配时的西蒙斯随机化回答模型。该模型在应用于总体为分层总体的时候比简单随机抽样下的西蒙斯模型有着更高的精度,在实际的调查操作中也有着更强的可行性。本文还探讨了受访者在不完全真实回答情况下的情形,并对模型进行了改进。     

消除回答误差的随机化模型

同其他统计调查方法一样,抽样调查结果也会出现误差。抽样调查的误差可分为抽样误差和非抽样误差两类,其中抽样误差是由抽样的随机性引起的,是不可消除的,但我们可以对它进行估计,并控制在要求的范围内。在样本量固定的情况下,我们通过采用适当的抽样方法和估计量来最大限度地减少抽样误差,并将其控制在一定的限度内。非抽样误差是发生在回答、登记、汇总、计算等过程中的误差,我们可以通过加强统计法规建设,提高统计人员的素质来消除或减少登记、汇总、计算等过程中的误差,而回答误差由于受多种因素的影响,非常难以控制和消除。在调查项目…     

“随机化回答”技术在逃税现象调查中的应用

一、导言在已有的文献中,我们一般很难发现有关逃税现象访问调查的研究,究其原因是因为逃税问题对受调查者而言非常敏感使调查无法奏效的缘故。事实上,在这种针对敏感性问题的调查中,如果只是使用常规性的调查方法,受调查者一般不愿配合调查者作出真实的回答,他们要么不提供真实性数据,要么干脆拒绝调查。前者因此而造成“有回答误差”,后者则造成“无回答误差”。两种“误差”的存在使得最终调查结果缺乏基本的说服力,更无法将其推而广之。能否设计出一种与众不同的调查方法,使得受调查者敢于将真实的数据“和盘托出”而不必担心承担任何后…     

企业市场调查中随机化回答技术的应用

随机化回答技术十分适宜调查敏感性问题并获得相关资料.在企业市场调查中应用随机化回答技术有利于获得敏感性资料,减少调查误差,提高企业竞争力和企业科学管理水平.文章以非工资性收入调查为例,说明了如何组合应用西蒙斯模型法与格林伯格非相关模型法.     

改进的随机化回答模型的贝叶斯方法

文章介绍了一种改进的敏感性问题随机化回答模型,并把贝叶斯方法用于该模型的参数估计,最后得出了参数的近似解。     

A Two-stage Binary Optional Randomized Response Model

Social desirability bias (SDB) is the tendency in respondents to answer questions untruthfully in the hope of giving good impression to others. SDB occurs when the survey question is highly sensitive or personal. The Randomized Response Technique (RRT) is one of several methods to get around SDB in face-to-face interview surveys involving sensitive questions. In this article, we first review some of the existing binary response RRT models. Then, we propose a new model—Two-Stage Binary Optional RRT model. Much of the focus is on estimating π, the prevalence of sensitive characteristic, and ω, the sensitivity level of the underlying question. We discuss the asymptotic properties of our estimators and present some simulation results. It turns out that the proposed Two-Stage Binary Optional RRT model is more effective than the Optional RRT model proposed by Gupta (2001).     

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ABSTRACT

In the class of stochastic volatility (SV) models, leverage effects are typically specified through the direct correlation between the innovations in both returns and volatility, resulting in the dynamic leverage (DL) model. Recently, two asymmetric SV models based on threshold effects have been proposed in the literature. As such models consider only the sign of the previous return and neglect its magnitude, this paper proposes a dynamic asymmetric leverage (DAL) model that accommodates the direct correlation as well as the sign and magnitude of the threshold effects. A special case of the DAL model with zero direct correlation between the innovations is the asymmetric leverage (AL) model. The dynamic asymmetric leverage models are estimated by the Monte Carlo likelihood (MCL) method. Monte Carlo experiments are presented to examine the finite sample properties of the estimator. For a sample size of T = 2000 with 500 replications, the sample means, standard deviations, and root mean squared errors of the MCL estimators indicate only a small finite sample bias. The empirical estimates for S&;P 500 and TOPIX financial returns, and USD/AUD and YEN/USD exchange rates, indicate that the DAL class, including the DL and AL models, is generally superior to threshold SV models with respect to AIC and BIC, with AL typically providing the best fit to the data.     

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This article presents a large class of probability densities f(x, θ) for which, with positive probability, the maximum likelihood estimator based on a sample of size 2 is non unique, and the possible values of do not form an interval. Such a density f(x, θ) can even be chosen to be unimodal, and one such example is the Cauchy density with a location parameter. A discrete version of the argument gives examples in which the nonuniqueness of the maximum likelihood estimator persists for samples of arbitrary size.     

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Selection processes that are inherent in litigation complicate policy-oriented research of medical malpractice tort reforms. In regard to their deterrent impact, the range of potential inferences based on analyses of claim frequency is limited because plaintiffs only file a subset of potential claims. In regard to their impact on litigation costs, researchers often analyze data on claim disposition, but it is difficult to determine whether effects attributed to tort reforms reflect changes in litigant behavior or their influence on the selection of claims. In this article, we evaluate these problems and report results of our study of the effects of medical malpractice reforms on claim disposition.