基于多階模型理論的非抽樣誤差研究
發(fā)布時(shí)間：2019-02-14 07:36
【摘要】：非抽樣誤差是統(tǒng)計(jì)調(diào)查中除抽樣誤差以外,由于各種原因而引起的誤差,對調(diào)查結(jié)果的影響非常大。本文結(jié)合中國統(tǒng)計(jì)調(diào)查的實(shí)際,全面研究了概率抽樣調(diào)查中非抽樣誤差產(chǎn)生的原因、測度方法及估計(jì)與調(diào)整方法。文章探討了非抽樣誤差產(chǎn)生的制度、文化和應(yīng)用等方面的原因,設(shè)計(jì)了非抽樣誤差在統(tǒng)計(jì)調(diào)查總誤差中所占比重的測度方法,研究了定量測度非抽樣誤差的方法路徑,應(yīng)用多階模型理論設(shè)計(jì)了概率抽樣調(diào)查中出現(xiàn)測量誤差和無回答誤差時(shí)的估計(jì)量以及對傳統(tǒng)估計(jì)結(jié)果的調(diào)整方法。本文的研究初步形成了研究概率抽樣調(diào)查中非抽樣誤差的內(nèi)容體系和方法體系。 在理論研究方面,本文以多階模型為方法體系的核心,并輔以路徑分析等其它研究方法。本文探索了將多階模型應(yīng)用于非抽樣誤差研究的思路。由多階段抽樣調(diào)查方式調(diào)查獲得的數(shù)據(jù)具有多階特征,適合采用多階模型方法進(jìn)行研究。當(dāng)出現(xiàn)測量誤差時(shí),本文吸收了多階模型中空模型的建模思想,設(shè)計(jì)了分層抽樣層均值和總均值方差的估計(jì)量。這部分研究還引入了測量可靠性指標(biāo),研究了可靠性對估計(jì)結(jié)果的影響。對于多階段調(diào)查數(shù)據(jù)中的多變量關(guān)系分析,本文討論了利用多階模型對存在測量誤差時(shí)多變量關(guān)系的估計(jì)與調(diào)整方法。當(dāng)出現(xiàn)無回答誤差時(shí),本文利用了多階模型分析多階段抽樣調(diào)查數(shù)據(jù)的原理,采用經(jīng)驗(yàn)加權(quán)方法估計(jì)分層抽樣中各層均值的估計(jì)量。本文還嘗試應(yīng)用虛擬變量和多階模型結(jié)合的方法研究出現(xiàn)無回答時(shí)的估計(jì)問題。 同時(shí),本文還采用了其它研究方法。如在研究基于設(shè)計(jì)的非抽樣誤差測度方法時(shí),本文以均方誤差的定義式為基礎(chǔ),論證了非抽樣誤差在統(tǒng)計(jì)調(diào)查總誤差中所占比重的測度方法,得出了該比重所在區(qū)間的下限,并編制了該比重與回答率和測量可靠性不同取值的對應(yīng)關(guān)系表；在研究基于模型的非抽樣誤差測度方法時(shí),文章運(yùn)用路徑分析法從非抽樣誤差產(chǎn)生的根源入手研究了包括多指標(biāo)和單指標(biāo)統(tǒng)計(jì)調(diào)查中非抽樣誤差的測度。此外,本文還推導(dǎo)了測量誤差方差的定量測度公式,設(shè)計(jì)了存在測量誤差時(shí)分層抽樣中各層均值的方差估計(jì)量。 在應(yīng)用研究方面,本文采用2007年廣東省城鎮(zhèn)住戶調(diào)查的11市和7縣、區(qū)的1600個(gè)家庭的消費(fèi)數(shù)據(jù)對主要理論進(jìn)行了實(shí)證檢驗(yàn),形成了一套利用多階模型研究非抽樣誤差的應(yīng)用體系。實(shí)證結(jié)果表明：當(dāng)組間差異顯著時(shí),應(yīng)該運(yùn)用多階模型進(jìn)行數(shù)據(jù)分析。在恰當(dāng)設(shè)計(jì)的程序中,多階模型能夠比傳統(tǒng)方法更好地“擬合”樣本數(shù)據(jù)的特征,實(shí)現(xiàn)非抽樣誤差的測度與調(diào)整。實(shí)證分析展示了多階模型視角下非抽樣誤差的研究路徑,給出了定量測度非抽樣誤差的模擬案例。
[Abstract]:Non-sampling error is the error caused by various reasons except sampling error in the statistical survey, which has a great influence on the investigation result. In this paper, the causes, measurement methods, estimation and adjustment methods of non-sampling errors in probabilistic sampling surveys are studied. This paper discusses the causes of the system, culture and application of the non-sampling error, designs the measurement method of the proportion of the non-sampling error in the total error of statistical investigation, and studies the method path of quantitative measurement of the non-sampling error. Based on the theory of multi-order model, the estimation of measurement error and no response error in probabilistic sampling survey and the adjustment method of traditional estimation results are designed. In this paper, the content system and method system of non-sampling error in probabilistic sampling survey are preliminarily formed. In the theoretical research, this paper takes the multi-order model as the core of the method system, and complements other research methods such as path analysis. In this paper, the idea of applying multi-order model to the study of non-sampling error is explored. The data obtained from multi-stage sampling survey have multi-order characteristics and are suitable to be studied by multi-order model method. When the measurement error occurs, this paper absorbs the modeling idea of the multi-order model hollow model, and designs the estimators of the stratified sampling layer mean and the total mean variance. In this part, the reliability index is introduced and the influence of reliability on the estimation results is studied. For the multivariable relation analysis of multistage survey data, this paper discusses the estimation and adjustment method of multivariable relationship in the presence of measurement error by using multi-order model. When there is no response error, the principle of multi-stage sampling data analysis is used in this paper, and the empirical weighting method is used to estimate the mean value of each layer in stratified sampling. This paper also attempts to use the method of virtual variable and multi-order model to study the estimation problem when there is no answer. At the same time, this paper also adopts other research methods. For example, in the study of the non-sampling error measurement method based on design, based on the definition of mean square error, this paper demonstrates the measurement method of the proportion of non-sampling error in the total error of statistical investigation, and obtains the lower limit of the proportion between the regions in which the proportion is located. At the same time, the corresponding relation table between the specific gravity, the response rate and the measurement reliability is worked out. In this paper, the non-sampling error measurement method based on the model is studied, and the non-sampling error measurement including multi-index and single-index statistical survey is studied by the path analysis method from the root of the non-sampling error. In addition, the quantitative measurement formula of measurement error variance is derived, and the variance estimator of the mean value in stratified sampling with measurement error is designed. In the aspect of applied research, this paper uses the consumption data of 1 600 households in 11 cities and 7 counties and districts of Guangdong Province in 2007 to make an empirical test on the main theories. A set of application system is formed to study the non-sampling error using multi-order model. The empirical results show that when the differences between groups are significant, multi-order model should be used for data analysis. In the properly designed program, the multi-order model can better "fit" the characteristics of the sample data than the traditional method, and realize the measurement and adjustment of the non-sampling error. The empirical analysis shows the research path of non-sampling error from the perspective of multi-order model, and gives a simulation case of quantitative measurement of non-sampling error.
【學(xué)位授予單位】：暨南大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2011
【分類號(hào)】：C811
本文編號(hào)：2421983
[Abstract]:Non-sampling error is the error caused by various reasons except sampling error in the statistical survey, which has a great influence on the investigation result. In this paper, the causes, measurement methods, estimation and adjustment methods of non-sampling errors in probabilistic sampling surveys are studied. This paper discusses the causes of the system, culture and application of the non-sampling error, designs the measurement method of the proportion of the non-sampling error in the total error of statistical investigation, and studies the method path of quantitative measurement of the non-sampling error. Based on the theory of multi-order model, the estimation of measurement error and no response error in probabilistic sampling survey and the adjustment method of traditional estimation results are designed. In this paper, the content system and method system of non-sampling error in probabilistic sampling survey are preliminarily formed. In the theoretical research, this paper takes the multi-order model as the core of the method system, and complements other research methods such as path analysis. In this paper, the idea of applying multi-order model to the study of non-sampling error is explored. The data obtained from multi-stage sampling survey have multi-order characteristics and are suitable to be studied by multi-order model method. When the measurement error occurs, this paper absorbs the modeling idea of the multi-order model hollow model, and designs the estimators of the stratified sampling layer mean and the total mean variance. In this part, the reliability index is introduced and the influence of reliability on the estimation results is studied. For the multivariable relation analysis of multistage survey data, this paper discusses the estimation and adjustment method of multivariable relationship in the presence of measurement error by using multi-order model. When there is no response error, the principle of multi-stage sampling data analysis is used in this paper, and the empirical weighting method is used to estimate the mean value of each layer in stratified sampling. This paper also attempts to use the method of virtual variable and multi-order model to study the estimation problem when there is no answer. At the same time, this paper also adopts other research methods. For example, in the study of the non-sampling error measurement method based on design, based on the definition of mean square error, this paper demonstrates the measurement method of the proportion of non-sampling error in the total error of statistical investigation, and obtains the lower limit of the proportion between the regions in which the proportion is located. At the same time, the corresponding relation table between the specific gravity, the response rate and the measurement reliability is worked out. In this paper, the non-sampling error measurement method based on the model is studied, and the non-sampling error measurement including multi-index and single-index statistical survey is studied by the path analysis method from the root of the non-sampling error. In addition, the quantitative measurement formula of measurement error variance is derived, and the variance estimator of the mean value in stratified sampling with measurement error is designed. In the aspect of applied research, this paper uses the consumption data of 1 600 households in 11 cities and 7 counties and districts of Guangdong Province in 2007 to make an empirical test on the main theories. A set of application system is formed to study the non-sampling error using multi-order model. The empirical results show that when the differences between groups are significant, multi-order model should be used for data analysis. In the properly designed program, the multi-order model can better "fit" the characteristics of the sample data than the traditional method, and realize the measurement and adjustment of the non-sampling error. The empirical analysis shows the research path of non-sampling error from the perspective of multi-order model, and gives a simulation case of quantitative measurement of non-sampling error.
【學(xué)位授予單位】：暨南大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2011
【分類號(hào)】：C811
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，本文編號(hào)：2421983
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