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Fieller Stability Measure: a novel model-dependent backtesting approach

Fieller Stability Measure: a novel model-dependent backtesting approach
Fieller Stability Measure: a novel model-dependent backtesting approach
Dataset shift is present in almost all real-world applications, since most of them are constantly dealing with changing environments. Detecting fractures in datasets on time allows recalibrating the models before a significant decrease in the model’s performance is observed. Since small changes are normal in most applications and do not justify the efforts that a model recalibration requires, we are only interested in identifying those changes that are critical for the correct functioning of the model. In this work we propose a model-dependent backtesting strategy designed to identify significant changes in the covariates, relating a confidence zone of the change to a maximal deviance measure obtained from the coefficients of the model. Using logistic regression as a predictive approach, we performed experiments on simulated data, and on a real-world credit scoring dataset. The results show that the proposed method has better performance than traditional approaches, consistently identifying major changes in variables while taking into account important characteristics of the problem, such as sample sizes and variances, and uncertainty in the coefficients
0160-5682
1895-1905
Bravo, Cristian
b22c4145-644e-40ee-85d8-431c59c3c71b
Maldonado, Sebastián
9e5fb121-d905-4337-beb3-bba6f7da9ae2
Bravo, Cristian
b22c4145-644e-40ee-85d8-431c59c3c71b
Maldonado, Sebastián
9e5fb121-d905-4337-beb3-bba6f7da9ae2

Bravo, Cristian and Maldonado, Sebastián (2015) Fieller Stability Measure: a novel model-dependent backtesting approach. Journal of the Operational Research Society, 66 (11), 1895-1905. (doi:10.1057/jors.2015.18).

Record type: Article

Abstract

Dataset shift is present in almost all real-world applications, since most of them are constantly dealing with changing environments. Detecting fractures in datasets on time allows recalibrating the models before a significant decrease in the model’s performance is observed. Since small changes are normal in most applications and do not justify the efforts that a model recalibration requires, we are only interested in identifying those changes that are critical for the correct functioning of the model. In this work we propose a model-dependent backtesting strategy designed to identify significant changes in the covariates, relating a confidence zone of the change to a maximal deviance measure obtained from the coefficients of the model. Using logistic regression as a predictive approach, we performed experiments on simulated data, and on a real-world credit scoring dataset. The results show that the proposed method has better performance than traditional approaches, consistently identifying major changes in variables while taking into account important characteristics of the problem, such as sample sizes and variances, and uncertainty in the coefficients

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More information

Published date: 15 April 2015
Organisations: Southampton Business School

Identifiers

Local EPrints ID: 396648
URI: http://eprints.soton.ac.uk/id/eprint/396648
ISSN: 0160-5682
PURE UUID: 0e24f3c3-04f6-4d87-b3d7-7ff2a8debe96
ORCID for Cristian Bravo: ORCID iD orcid.org/0000-0003-1579-1565

Catalogue record

Date deposited: 09 Jun 2016 13:16
Last modified: 20 Jul 2019 00:48

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