【摘要】：2008年美國的次貸危機(jī)演變成一場全球金融危機(jī)，世界各國經(jīng)濟(jì)都遭受到嚴(yán)重的打擊。與此同時(shí)，中國爆發(fā)了“三鹿奶粉”事件，造成中國民族乳業(yè)品牌的一次集體大潰敗,對(duì)中國乳業(yè)國際聲譽(yù)的產(chǎn)生了致命影響。食品企業(yè)這些問題嚴(yán)重影響了食品行業(yè)信用情況，一家企業(yè)受到食品安全問題的影響后，企業(yè)信用可以說是降到谷底，食品企業(yè)如何徹底應(yīng)對(duì)這一危機(jī)影響，成為企業(yè)信用建設(shè)的重點(diǎn)問題。 本文以信用評(píng)級(jí)的初始步驟一一數(shù)據(jù)預(yù)處理為出發(fā)點(diǎn)，研究有缺失數(shù)據(jù)的數(shù)據(jù)集的填補(bǔ)問題。過去的企業(yè)信用評(píng)分研究多著眼于模型的設(shè)計(jì)，而忽視了對(duì)不完整數(shù)據(jù)預(yù)處理的研究。事實(shí)上，比較成熟的建模方法對(duì)其處理的數(shù)據(jù)集都有一定的要求，如數(shù)據(jù)完整性好、冗余性低、具有代表性等。數(shù)據(jù)缺失是信用數(shù)據(jù)中普遍存在的問題，對(duì)它的處理已成為信用評(píng)分研究中的關(guān)鍵問題。在我們做信用評(píng)級(jí)的研究過程中，難免會(huì)遇到這樣那樣的數(shù)據(jù)缺失問題，那么如何處理這一問題就成為了影響后續(xù)研究的關(guān)鍵問題。本文根據(jù)前人的經(jīng)驗(yàn)和研究結(jié)果，運(yùn)用基于貝葉斯理論的多重填補(bǔ)法來對(duì)有缺失數(shù)據(jù)的數(shù)據(jù)集進(jìn)行填補(bǔ)，然后在對(duì)填補(bǔ)后的數(shù)據(jù)進(jìn)行統(tǒng)計(jì)分析，最后將數(shù)據(jù)代入到信用評(píng)級(jí)模型中，來得到食品企業(yè)的信用狀況。本文運(yùn)用SAS統(tǒng)計(jì)軟件對(duì)缺失數(shù)據(jù)集進(jìn)行填補(bǔ)處理，得出的結(jié)果給出了5組填補(bǔ)值，分別計(jì)算得出29個(gè)食品企業(yè)的信用評(píng)分的排名情況，本文認(rèn)為，，這種方法在一定程度上克服了單一填補(bǔ)法在數(shù)據(jù)填補(bǔ)上的局限性，很好的處理了缺失數(shù)據(jù)的不確定性，為后續(xù)研究奠定了堅(jiān)實(shí)的數(shù)據(jù)基礎(chǔ)。 本文通過填補(bǔ)方法的介紹，食品企業(yè)信用現(xiàn)狀的分析，闡述了數(shù)據(jù)填補(bǔ)對(duì)于評(píng)級(jí)研究的意義，最后用實(shí)證研究給出了填補(bǔ)后的研究結(jié)果，與傳統(tǒng)的方法相比，本文的方法具有示范性的作用，能更好的運(yùn)用到食品企業(yè)信用評(píng)級(jí)中去，甚至對(duì)所有樣本的數(shù)據(jù)缺失都具有可操作性。
[Abstract]:In 2008, the subprime mortgage crisis in the United States became a global financial crisis, and the world economy suffered a serious blow. At the same time, the "Sanlu milk powder" incident broke out in China, resulting in a collective debacle of the Chinese national dairy brand, which had a fatal impact on the international reputation of the Chinese dairy industry. These problems have seriously affected the credit situation of the food industry. After an enterprise is affected by the food safety problem, the enterprise credit can be said to have dropped to the bottom. How do the food enterprises deal with the impact of this crisis thoroughly? Become the key issue of enterprise credit construction. This paper takes the initial step of credit rating-data preprocessing as the starting point, and studies the filling problem of data sets with missing data. In the past, the research of enterprise credit score mostly focused on the design of model, but ignored the research of incomplete data preprocessing. In fact, the mature modeling methods have certain requirements for the data set, such as good data integrity, low redundancy, representative and so on. The lack of data is a common problem in credit data, and the processing of it has become a key issue in the research of credit rating. In the course of our credit rating research, we will inevitably encounter the problem of data loss, so how to deal with this problem has become a key issue affecting the follow-up research. According to the previous experience and research results, this paper uses the multi-filling method based on Bayesian theory to fill the data set with missing data, and then carries on the statistical analysis to the data after filling. Finally, the data are added to the credit rating model to get the credit status of food enterprises. In this paper, SAS statistical software is used to fill the missing data set, the result gives five groups of filling values, and the credit rating of 29 food enterprises is calculated respectively. To some extent, this method overcomes the limitation of single filling method in data filling, deals with the uncertainty of missing data well, and lays a solid data foundation for further research. Through the introduction of the filling method and the analysis of the credit status of food enterprises, this paper expounds the significance of data filling for rating research, and finally gives the results of the filled research by empirical research, compared with the traditional method. The method presented in this paper can be applied to the credit rating of food enterprises better, and even can be operated on the lack of data of all samples.
【學(xué)位授予單位】：湖南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：F832.4;F426.82

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