Credit Risk Assessment Using Model-Based Clustering

碩士 === 國立東華大學 === 應用數學系 === 98 === This paper used the Gaussian mixture model to find credit risk. The author referred to Fraley and Raftery (2002), used the covariance that parameterized by eigenvalue decomposition and got ten models. As for the variables, the author extracted 22 variables from Alt...

Full description

Bibliographic Details
Main Authors:	Jia-Hao Syu, 許家豪
Other Authors:	C.K. Chu
Format:	Others
Language:	zh-TW
Published:	2010
Online Access:	http://ndltd.ncl.edu.tw/handle/54588626970986075964

id	ndltd-TW-098NDHU5507080
record_format	oai_dc
spelling	ndltd-TW-098NDHU55070802016-04-22T04:23:10Z http://ndltd.ncl.edu.tw/handle/54588626970986075964 Credit Risk Assessment Using Model-Based Clustering 基於模型分群之信用風險評估模式 Jia-Hao Syu 許家豪碩士國立東華大學應用數學系 98 This paper used the Gaussian mixture model to find credit risk. The author referred to Fraley and Raftery (2002), used the covariance that parameterized by eigenvalue decomposition and got ten models. As for the variables, the author extracted 22 variables from Altman (1968), Shumway (2001), Duffie (2007), Compbell (2008), and several financial related books. The author selected five variables and collocated with the ten Gaussian mixture models. The result indicated that the VEI model performed well combined with the variables that the author found. Compared with the classification of TEJ TCRI, the empirical result indicated that the author’s classification result had better classified result. C.K. Chu 朱至剛 2010 學位論文 ; thesis 47 zh-TW
collection	NDLTD
language	zh-TW
format	Others
sources	NDLTD
description	碩士 === 國立東華大學 === 應用數學系 === 98 === This paper used the Gaussian mixture model to find credit risk. The author referred to Fraley and Raftery (2002), used the covariance that parameterized by eigenvalue decomposition and got ten models. As for the variables, the author extracted 22 variables from Altman (1968), Shumway (2001), Duffie (2007), Compbell (2008), and several financial related books. The author selected five variables and collocated with the ten Gaussian mixture models. The result indicated that the VEI model performed well combined with the variables that the author found. Compared with the classification of TEJ TCRI, the empirical result indicated that the author’s classification result had better classified result.
author2	C.K. Chu
author_facet	C.K. Chu Jia-Hao Syu 許家豪
author	Jia-Hao Syu 許家豪
spellingShingle	Jia-Hao Syu 許家豪 Credit Risk Assessment Using Model-Based Clustering
author_sort	Jia-Hao Syu
title	Credit Risk Assessment Using Model-Based Clustering
title_short	Credit Risk Assessment Using Model-Based Clustering
title_full	Credit Risk Assessment Using Model-Based Clustering
title_fullStr	Credit Risk Assessment Using Model-Based Clustering
title_full_unstemmed	Credit Risk Assessment Using Model-Based Clustering
title_sort	credit risk assessment using model-based clustering
publishDate	2010
url	http://ndltd.ncl.edu.tw/handle/54588626970986075964
work_keys_str_mv	AT jiahaosyu creditriskassessmentusingmodelbasedclustering AT xǔjiāháo creditriskassessmentusingmodelbasedclustering AT jiahaosyu jīyúmóxíngfēnqúnzhīxìnyòngfēngxiǎnpínggūmóshì AT xǔjiāháo jīyúmóxíngfēnqúnzhīxìnyòngfēngxiǎnpínggūmóshì
_version_	1718230039895474176

Credit Risk Assessment Using Model-Based Clustering

Similar Items