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CREDIT RISK ANALYSIS FOR THE TELECOMMUNICATION COMPANIES OF RUSSIA: STATISTICAL MODEL. / Dengov, Viktor; Tulyakova, Irina.

2nd International Multidisciplinary Scientific Conference on Social Sciences and Arts SGEM2015. STEF92 Technology Ltd., 2015.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearch

Harvard

Dengov, V & Tulyakova, I 2015, CREDIT RISK ANALYSIS FOR THE TELECOMMUNICATION COMPANIES OF RUSSIA: STATISTICAL MODEL. in 2nd International Multidisciplinary Scientific Conference on Social Sciences and Arts SGEM2015. STEF92 Technology Ltd. https://doi.org/10.5593/SGEMSOCIAL2015/B22/S6.018

APA

Dengov, V., & Tulyakova, I. (2015). CREDIT RISK ANALYSIS FOR THE TELECOMMUNICATION COMPANIES OF RUSSIA: STATISTICAL MODEL. In 2nd International Multidisciplinary Scientific Conference on Social Sciences and Arts SGEM2015 STEF92 Technology Ltd.. https://doi.org/10.5593/SGEMSOCIAL2015/B22/S6.018

Vancouver

Dengov V, Tulyakova I. CREDIT RISK ANALYSIS FOR THE TELECOMMUNICATION COMPANIES OF RUSSIA: STATISTICAL MODEL. In 2nd International Multidisciplinary Scientific Conference on Social Sciences and Arts SGEM2015. STEF92 Technology Ltd. 2015 https://doi.org/10.5593/SGEMSOCIAL2015/B22/S6.018

Author

Dengov, Viktor ; Tulyakova, Irina. / CREDIT RISK ANALYSIS FOR THE TELECOMMUNICATION COMPANIES OF RUSSIA: STATISTICAL MODEL. 2nd International Multidisciplinary Scientific Conference on Social Sciences and Arts SGEM2015. STEF92 Technology Ltd., 2015.

BibTeX

@inproceedings{c932bffc03a648259b0dc785726e4493,
title = "CREDIT RISK ANALYSIS FOR THE TELECOMMUNICATION COMPANIES OF RUSSIA: STATISTICAL MODEL",
abstract = "In the first of our articles devoted to the evaluation of the credit risks of Russian telecommunication agencies we have studied the structure of data, carried out the financial and economic evaluation of the objects of our study, established the relationships between the indicators and chose the most representative of them. To do this the authors used the methods of financial and correlation analyses. In this article, we use the statistical model in order to evaluate the credit risks on the basis of those chosen indicators The final article of this cycle describes the linguistic model of credit risk estimation and provides the comparison of the results obtained through both models.",
keywords = "CLUSTER ANALYSIS, DISCRIMINANT ANALYSIS, MULTIDIMENSIONALITY OF REAL OBJECTS",
author = "Viktor Dengov and Irina Tulyakova",
year = "2015",
doi = "10.5593/SGEMSOCIAL2015/B22/S6.018",
language = "English",
isbn = "9786197105476",
booktitle = "2nd International Multidisciplinary Scientific Conference on Social Sciences and Arts SGEM2015",
publisher = "STEF92 Technology Ltd.",
address = "Bulgaria",

}

RIS

TY - GEN

T1 - CREDIT RISK ANALYSIS FOR THE TELECOMMUNICATION COMPANIES OF RUSSIA: STATISTICAL MODEL

AU - Dengov, Viktor

AU - Tulyakova, Irina

PY - 2015

Y1 - 2015

N2 - In the first of our articles devoted to the evaluation of the credit risks of Russian telecommunication agencies we have studied the structure of data, carried out the financial and economic evaluation of the objects of our study, established the relationships between the indicators and chose the most representative of them. To do this the authors used the methods of financial and correlation analyses. In this article, we use the statistical model in order to evaluate the credit risks on the basis of those chosen indicators The final article of this cycle describes the linguistic model of credit risk estimation and provides the comparison of the results obtained through both models.

AB - In the first of our articles devoted to the evaluation of the credit risks of Russian telecommunication agencies we have studied the structure of data, carried out the financial and economic evaluation of the objects of our study, established the relationships between the indicators and chose the most representative of them. To do this the authors used the methods of financial and correlation analyses. In this article, we use the statistical model in order to evaluate the credit risks on the basis of those chosen indicators The final article of this cycle describes the linguistic model of credit risk estimation and provides the comparison of the results obtained through both models.

KW - CLUSTER ANALYSIS

KW - DISCRIMINANT ANALYSIS

KW - MULTIDIMENSIONALITY OF REAL OBJECTS

U2 - 10.5593/SGEMSOCIAL2015/B22/S6.018

DO - 10.5593/SGEMSOCIAL2015/B22/S6.018

M3 - Conference contribution

SN - 9786197105476

BT - 2nd International Multidisciplinary Scientific Conference on Social Sciences and Arts SGEM2015

PB - STEF92 Technology Ltd.

ER -

ID: 3944947