Date of Award

4-2018

Degree Name

Doctor of Philosophy

Department

Statistics

First Advisor

Dr. Joshua D. Naranjo

Second Advisor

Dr. Magdalena Niewiadomska-Bugaj

Third Advisor

Dr. Clifton E. Ealy

Fourth Advisor

Dr. Jeff Terpstra

Keywords

Population stability index, Kullback-Liebler, divergence, risk management, model management, information theory

Abstract

Population stability is an important concept in model management. It is crucial to monitor whether the current population has changed from the population used during development of a model. For example, has the distribution of credit scores changed, and is the existing credit score model still valid? Population change may occur for many reasons–change in the economic environment, strategic change in the business, policy changes within the company, or changes in regulatory environment.

The population stability index (PSI) is a statistic that measures how much a variable has shifted over time, and is used to monitor applicability of a statistical model to the current population. In banking for example, a high PSI may result in an internal investigation of the reasons behind the change, or an audit by the Federal Reserve Bank. Since banks are heavily regulated by FRB, an unsuitable use of a model means additional risk.

There are not many studies about the statistical properties of PSI. Existing rules of thumb are: PSI < 0.10 means "little shift", .100.25 means "significant shift, action required". However, these benchmarks are being used without reference to Type I or Type II error rates. This thesis will try to fill the gap by providing statistical properties of PSI and some recommendations for the rules of thumb.

Access Setting

Dissertation-Open Access

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