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Linear Discriminant Analysis

In Linear Discriminant Analysis, variables are selected based on their estimated contribution to the likelihood of default.

What is Linear Discriminant Analysis?

Statistical modelling has a number of options available and Linear discriminant analysis (LDA) is one of the most popular ones used for developing models. In LDA, variables are selected based on their estimated contribution to the likelihood of default. The derivation of default drivers comes from an extensive pool of qualitative features and accounting ratios.

Example of Linear Discriminant Analysis:

Altman Z Score model is one of the classic examples of LDA. The contributions (i.e. weights) of each accounting ratio to the overall score are represented by Altman’s Z-score.

Why is Linear Discriminant Analysis important?

Discriminant Analysis provides a framework on how we can differentiate between relevant and irrelevant factors for predicting the default and assist risk managers in building the models for default prediction.

Owais Siddiqui
1 min read
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