發(fā)布時(shí)間：2018-09-05 19:00

【摘要】：在行為學(xué)、社會(huì)學(xué)、心理測(cè)量學(xué)以及經(jīng)濟(jì)管理等研究領(lǐng)域,經(jīng)常會(huì)涉及到一些難以直接準(zhǔn)確測(cè)量的變量,如智力、學(xué)習(xí)動(dòng)機(jī)等因素,需要評(píng)估這些潛在變量與外顯變量之間的關(guān)系,對(duì)于這些問(wèn)題,傳統(tǒng)的統(tǒng)計(jì)分析方法難以解決。結(jié)構(gòu)方程模型(Structural Equation Modeling,簡(jiǎn)稱SEM)是多元統(tǒng)計(jì)分析的一個(gè)重要工具,相比傳統(tǒng)的回歸分析,結(jié)構(gòu)方程模型不僅能夠度量外顯變量與潛在因子之間的關(guān)系,同時(shí)還能夠進(jìn)一步刻畫(huà)潛在變量之間復(fù)雜的非線性結(jié)構(gòu)。在經(jīng)典的回歸分析中,其通常假定自變量為非隨機(jī)的,而結(jié)構(gòu)方程模型卻沒(méi)有這種假定,若各影響因子可以直接觀測(cè),則結(jié)構(gòu)方程模型就退化為回歸分析,此外,結(jié)構(gòu)方程模型還允許自變量和因變量存在測(cè)量誤差,可以同時(shí)估計(jì)因子結(jié)構(gòu)以及因子關(guān)系,容許更大彈性的測(cè)量模型。本文主要研究了非線性結(jié)構(gòu)方程模型的貝葉斯統(tǒng)計(jì)推斷問(wèn)題,其研究?jī)?nèi)容大致分為如下三個(gè)方面:(1)非線性結(jié)構(gòu)方程模型的貝葉斯分析;(2)有限混合結(jié)構(gòu)方程模型分析的貝葉斯分析;(3)空間結(jié)構(gòu)方程模型的貝葉斯分析。就研究?jī)?nèi)容而言,由于模型的復(fù)雜性和潛在變量的影響,模型的似然函數(shù)涉及到難以處理的多重積分。為此,本文建立起完整的貝葉斯后驗(yàn)抽樣程序并采用了結(jié)合Gibbs抽樣和MH算法的MCMC技術(shù)以實(shí)現(xiàn)參數(shù)估計(jì);由于外顯變量的異質(zhì)性,傳統(tǒng)的單個(gè)總體的假設(shè)往往并不成立,為了解決這一問(wèn)題,本文建立起有限混合結(jié)構(gòu)方程模型和相應(yīng)的后驗(yàn)推斷程序,眾所周知,在有限混合建模中,由于“Label switching"問(wèn)題常常會(huì)導(dǎo)致有偏甚至無(wú)效的統(tǒng)計(jì)推斷結(jié)論,為此,通過(guò)數(shù)據(jù)添加策略,建立起關(guān)于指標(biāo)變量的完全數(shù)據(jù)似然,并有針對(duì)性的采用了狄利克雷先驗(yàn)等先驗(yàn)設(shè)置獲得混合比例的參數(shù)估計(jì);在此基礎(chǔ)上,本文進(jìn)一步將前述研究成果推廣至含有空間隨機(jī)效應(yīng)的結(jié)構(gòu)方程模型的分析中,采用了空間條件自回歸模型刻畫(huà)區(qū)域異質(zhì)性及相關(guān)關(guān)系,得到空間隨機(jī)效應(yīng)的估計(jì)。最后,本文應(yīng)用老撾某大學(xué)的學(xué)生成績(jī)的實(shí)例來(lái)說(shuō)明上述方法的有效性,其相關(guān)研究成果對(duì)于老撾政府在高等教育方面的政策制定和財(cái)政投入均有一定的參考作用。本文的選題來(lái)源于實(shí)際,其工作是對(duì)當(dāng)代貝葉斯分析的推廣和發(fā)展,豐富了貝葉斯方法的內(nèi)涵和應(yīng)用范圍,其中涉及到的一些關(guān)鍵技術(shù)如貝葉斯有限混合建模、空間條件自回歸建模等均是有針對(duì)性的研究策略,適應(yīng)了實(shí)際問(wèn)題中對(duì)復(fù)雜數(shù)據(jù)分析的需要。
[Abstract]:In the fields of behavior, sociology, psychometrics and economic management, there are often variables that are difficult to measure directly and accurately, such as intelligence, learning motivation, and so on. The relationship between these potential variables and explicit variables needs to be evaluated. Traditional statistical analysis methods are difficult to solve these problems. Structural equation model (Structural Equation Modeling,) is an important tool for multivariate statistical analysis. Compared with traditional regression analysis, structural equation model can not only measure the relationship between explicit variables and potential factors. At the same time, it can further describe the complex nonlinear structure between potential variables. In classical regression analysis, the independent variables are usually assumed to be non-random, but the structural equation model does not. If the influence factors can be observed directly, the structural equation model is reduced to regression analysis. The structural equation model also allows for the existence of measurement errors between independent variables and dependent variables, and can simultaneously estimate factor structures and factor relationships, allowing for more elastic measurement models. In this paper, the Bayesian statistical inference problem of nonlinear structural equation model is studied. The research contents are as follows: (1) Bayesian analysis of nonlinear structural equation model; (2) Bayesian analysis of finite mixed structural equation model; (3) Bayesian analysis of spatial structural equation model. As far as the research content is concerned, due to the complexity of the model and the influence of the potential variables, the likelihood function of the model involves multiple integrals which are difficult to deal with. In this paper, a complete Bayesian posteriori sampling procedure is established and the MCMC technique combining Gibbs sampling and MH algorithm is used to realize parameter estimation. Because of the heterogeneity of explicit variables, the traditional assumption of a single population is often not true. In order to solve this problem, the finite mixed structural equation model and the corresponding posteriori inference program are established in this paper. It is well known that in the finite hybrid modeling, the "Label switching" problem often leads to partial or even invalid statistical inference conclusions. For this reason, through the strategy of adding data, the complete data likelihood of index variable is established, and the parameter estimation of mixed ratio is obtained by using prior settings such as Dilikere priori. In this paper, the above research results are extended to the analysis of structural equation models with spatial random effects. Spatial conditional autoregressive models are used to characterize regional heterogeneity and correlation, and the estimation of spatial random effects is obtained. Finally, this paper applies the example of student achievement of a university in Laos to illustrate the effectiveness of the above method, and its related research results have a certain reference role for the policy formulation and financial investment of the Lao government in higher education. The work of this paper is to popularize and develop modern Bayesian analysis, which enriches the connotation and application scope of Bayesian method, and involves some key technologies such as Bayesian finite hybrid modeling. Spatial conditional autoregressive modeling is a targeted research strategy, which meets the needs of complex data analysis in practical problems.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：C81;G649.334

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 唐穎;張慧琴;;基于SEM結(jié)構(gòu)方程的區(qū)域科技競(jìng)爭(zhēng)力評(píng)價(jià)模型構(gòu)建[J];科學(xué)管理研究;2013年01期

2 謝英;;老撾語(yǔ)專業(yè)教學(xué)改革實(shí)踐探析[J];經(jīng)濟(jì)與社會(huì)發(fā)展;2011年06期

3 衛(wèi)彥雄;;我國(guó)老撾語(yǔ)本科教學(xué)中存在的問(wèn)題及其對(duì)策——以廣西民族大學(xué)教學(xué)實(shí)踐為例[J];經(jīng)濟(jì)與社會(huì)發(fā)展;2011年05期

4 吳林海;侯博;高申榮;;基于結(jié)構(gòu)方程模型的分散農(nóng)戶農(nóng)藥殘留認(rèn)知與主要影響因素分析[J];中國(guó)農(nóng)村經(jīng)濟(jì);2011年03期

5 孔榮;王亞軍;;農(nóng)戶集中居住意愿的影響因素分析[J];新疆農(nóng)墾經(jīng)濟(jì);2010年08期

6 武文杰;劉志林;張文忠;;基于結(jié)構(gòu)方程模型的北京居住用地價(jià)格影響因素評(píng)價(jià)[J];地理學(xué)報(bào);2010年06期

7 梁一鳴;張鈺爛;董西釧;;基于結(jié)構(gòu)方程模型的杭州城鎮(zhèn)居民食品安全滿意度統(tǒng)計(jì)評(píng)估[J];統(tǒng)計(jì)教育;2010年05期

8 林彥蕓;;基于結(jié)構(gòu)方程模型的廣東省中學(xué)教師參與體育活動(dòng)影響因素分析[J];廣州體育學(xué)院學(xué)報(bào);2010年02期

9 趙夫明;王學(xué)臣;胡云江;;結(jié)構(gòu)方程模式在心理學(xué)研究中的適用性評(píng)價(jià)[J];重慶科技學(xué)院學(xué)報(bào)(社會(huì)科學(xué)版);2010年02期

10 劉彪;舒劍萍;;基于結(jié)構(gòu)方程模型的高校教職工心理癥狀及其影響因素的相關(guān)分析[J];數(shù)理醫(yī)藥學(xué)雜志;2009年06期

相關(guān)博士學(xué)位論文 前1條

1 鄭術(shù)蓉;線性不等式約束下的EM算法[D];吉林大學(xué);2004年

相關(guān)碩士學(xué)位論文 前3條

1 王英;基于方差和中值調(diào)整的RNA-Seq數(shù)據(jù)標(biāo)準(zhǔn)化方法及其評(píng)估[D];廈門(mén)大學(xué);2014年

2 寇鵬;基于非線性結(jié)構(gòu)方程模型的公司成長(zhǎng)性分析[D];昆明理工大學(xué);2013年

3 周旭武;結(jié)構(gòu)方程模型方法的實(shí)現(xiàn)與應(yīng)用[D];大連理工大學(xué);2009年

，

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