本文選題：相關(guān)性研究 + 非參數(shù)模型　； 參考：《浙江工商大學(xué)》2017年碩士論文

【摘要】：相關(guān)性分析是研究數(shù)據(jù)之間關(guān)系的一種方法,是變量隨機(jī)分析的一個(gè)重要課題,而相關(guān)性分析的結(jié)果能夠?yàn)榘l(fā)掘數(shù)據(jù)背后的信息提供有力地支持。從應(yīng)用角度來(lái)看,現(xiàn)在金融保險(xiǎn)等領(lǐng)域的投資風(fēng)控、信貸評(píng)估,網(wǎng)絡(luò)及APP領(lǐng)域的信息推送等等均和相關(guān)性分析有著緊密聯(lián)系。早先學(xué)者們對(duì)相關(guān)性進(jìn)行了研究,提出了許多度量變量相關(guān)性的方法,但這些相關(guān)性研究主要關(guān)注變量之間相關(guān)程度的分析,而對(duì)于變量之間相關(guān)模式的識(shí)別與研究并不是很重視�，F(xiàn)有的一些相關(guān)系數(shù),如Pearson相關(guān)系數(shù),能對(duì)變量間的相關(guān)關(guān)系進(jìn)行度量但不能對(duì)變量的相關(guān)模式進(jìn)行識(shí)別,另一些相關(guān)系數(shù),如Kendall相關(guān)系數(shù)、Spearman相關(guān)系數(shù)等,雖然可以一定程度上反映變量之間的相關(guān)關(guān)系,但僅僅片面地刻畫了變量之間的相關(guān)結(jié)構(gòu)。數(shù)據(jù)時(shí)代的到來(lái)給變量之間的相關(guān)性研究帶來(lái)了挑戰(zhàn)。從理論上來(lái)看,多個(gè)變量之間的相關(guān)性關(guān)系非常復(fù)雜,對(duì)于高維的數(shù)據(jù)更是如此。隨著研究的深入,有學(xué)者發(fā)現(xiàn)原有的一些研究假設(shè)并不成立,這些不恰當(dāng)?shù)募僭O(shè)可能會(huì)導(dǎo)致嚴(yán)重的后果。本文受許冰(2010)路徑設(shè)計(jì)的啟發(fā),并借鑒近來(lái)的一些研究成果,通過(guò)構(gòu)建路徑模型體系,綜合考察變量間的相關(guān)模式及相關(guān)性的度量,進(jìn)而對(duì)變量進(jìn)行路徑相關(guān)性分析,為變量間的相關(guān)性分析提供一種新的方法。本文使用Li and Racine(2004)的非參數(shù)變量篩選方法,對(duì)有關(guān)變量進(jìn)行了分類;基于變量篩選結(jié)果構(gòu)建非參數(shù)路徑模型體系,分析變量間的整體效應(yīng)、直接效應(yīng)和間接效應(yīng)。發(fā)現(xiàn):(1)不管是在基準(zhǔn)模型還是路徑模型中,非線性分量的占比大于線性分量的占比,線性分量的波動(dòng)大于非線性分量的波動(dòng),且非線性變量在模型中占主導(dǎo)地位;(2)單路徑變量中用電量的整體效應(yīng)最大,雙路徑變量中用電量和已用授信額度的整體效應(yīng)最大;(3)用基準(zhǔn)模型的外推精度取代變量間的因果分析,對(duì)具體數(shù)據(jù)進(jìn)行了模擬分析。
[Abstract]:Correlation analysis is a method to study the relationship between data. It is an important subject of variable random analysis, and the result of correlation analysis can provide strong support for the information behind the data. From the perspective of application, the investment wind control, credit assessment, network and APP field of investment in financial insurance and other fields are pushed forward. There is a close relationship between equality and correlation analysis. Earlier scholars have studied the correlation and put forward a number of methods to measure the correlation of variables, but these correlation studies mainly focus on the analysis of the correlation between variables, but not much attention is paid to the identification and research of correlation patterns between variables. A number, such as the correlation coefficient of Pearson, can measure the correlation between variables but can not identify the correlation patterns of variables. Other correlation coefficients, such as Kendall correlation coefficient and Spearman correlation coefficient, can reflect the correlation between variables to a certain extent, but only one-sided depicts the correlation structure between variables. The arrival of the data age challenges the correlation between variables. In theory, the correlation between multiple variables is very complex, and it is more so for high dimensional data. As the research goes deep, some scholars have found that some of the original hypotheses are not established, and these inappropriate hypotheses may lead to serious problems. This article is inspired by the design of Xu ice (2010) path, and draws on some recent research results. Through the construction of the path model system, this paper comprehensively investigates the correlation patterns and correlation between variables, and then carries out path correlation analysis on variables, and provides a new method for correlation analysis among variables. This paper uses Li and Racine (2004) the non parametric variable selection method is used to classify the related variables; based on the variable screening results, the non parametric path model system is constructed, and the overall effect, the direct effect and the indirect effect between the variables are analyzed. (1) the proportion of nonlinear components is greater than the proportion of the linear component in the reference model or the path model. The fluctuation of component is greater than the fluctuation of nonlinear component, and the nonlinear variable is dominant in the model. (2) the overall effect of electricity consumption in single path variable is the largest, and the total effect of electricity consumption and credit line is the largest in the double path variable. (3) the effect analysis is replaced by the extrapolation precision of the reference model, and the specific data are carried out. The simulation analysis.
【學(xué)位授予單位】：浙江工商大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：C81

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