基于贝叶斯几何回归和贝叶斯混合几何回归的信用支付频率模型

IF 0.3 Q4 MATHEMATICS
Ikacipta Mega Ayuputri, Nur Iriawan, P. P. Oktaviana
{"title":"基于贝叶斯几何回归和贝叶斯混合几何回归的信用支付频率模型","authors":"Ikacipta Mega Ayuputri, Nur Iriawan, P. P. Oktaviana","doi":"10.11113/MATEMATIKA.V34.N3.1143","DOIUrl":null,"url":null,"abstract":"In distributing funds to customers as credit, multi-finance companies have two necessary risks, i.e. prepayment risk, and default risk. The default risk can be minimized by determining the factors that affect the survival of customers to make credit payment, in terms of frequency of credit payments by customers that are distributed geometry. The proposed modelling is using Bayesian Geometric Regression and Bayesian Mixture Geometric Regression. The best model of this research is modelling using Bayesian Geometric Regression method because it has lower DIC values than Bayesian Mixture Geometric Regression. Modelling using Bayesian Geometric Regression show the significant variables are marital status, down payment, installment length, length of stay, and insurance.","PeriodicalId":43733,"journal":{"name":"Matematika","volume":null,"pages":null},"PeriodicalIF":0.3000,"publicationDate":"2018-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Frequency Model of Credit Payment using Bayesian Geometric Regression and Bayesian Mixture Geometric Regression\",\"authors\":\"Ikacipta Mega Ayuputri, Nur Iriawan, P. P. Oktaviana\",\"doi\":\"10.11113/MATEMATIKA.V34.N3.1143\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In distributing funds to customers as credit, multi-finance companies have two necessary risks, i.e. prepayment risk, and default risk. The default risk can be minimized by determining the factors that affect the survival of customers to make credit payment, in terms of frequency of credit payments by customers that are distributed geometry. The proposed modelling is using Bayesian Geometric Regression and Bayesian Mixture Geometric Regression. The best model of this research is modelling using Bayesian Geometric Regression method because it has lower DIC values than Bayesian Mixture Geometric Regression. Modelling using Bayesian Geometric Regression show the significant variables are marital status, down payment, installment length, length of stay, and insurance.\",\"PeriodicalId\":43733,\"journal\":{\"name\":\"Matematika\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.3000,\"publicationDate\":\"2018-12-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Matematika\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.11113/MATEMATIKA.V34.N3.1143\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"MATHEMATICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Matematika","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.11113/MATEMATIKA.V34.N3.1143","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MATHEMATICS","Score":null,"Total":0}
引用次数: 0

摘要

多元金融公司在将资金作为信贷分配给客户的过程中,必然存在两种风险,即提前还款风险和违约风险。通过确定影响客户进行信用支付的生存因素,以分布几何形状的客户的信用支付频率为依据,可以将违约风险最小化。所提出的模型是使用贝叶斯几何回归和贝叶斯混合几何回归。本研究的最佳模型是使用贝叶斯几何回归方法建模,因为它比贝叶斯混合几何回归具有更低的DIC值。贝叶斯几何回归模型显示,婚姻状况、首付款、分期付款时间、住宿时间和保险是显著变量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Frequency Model of Credit Payment using Bayesian Geometric Regression and Bayesian Mixture Geometric Regression
In distributing funds to customers as credit, multi-finance companies have two necessary risks, i.e. prepayment risk, and default risk. The default risk can be minimized by determining the factors that affect the survival of customers to make credit payment, in terms of frequency of credit payments by customers that are distributed geometry. The proposed modelling is using Bayesian Geometric Regression and Bayesian Mixture Geometric Regression. The best model of this research is modelling using Bayesian Geometric Regression method because it has lower DIC values than Bayesian Mixture Geometric Regression. Modelling using Bayesian Geometric Regression show the significant variables are marital status, down payment, installment length, length of stay, and insurance.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Matematika
Matematika MATHEMATICS-
自引率
25.00%
发文量
0
审稿时长
24 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信