Statistical Hypothesis Testing for Information Value (IV)
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866915757912227840 |
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| author | Rojas, Helder Alvarez, Cirilo Rojas, Nilton |
| author_facet | Rojas, Helder Alvarez, Cirilo Rojas, Nilton |
| contents | Information Value (IV) is a widely used technique for feature selection prior to the modeling phase, particularly in credit scoring and related domains. However, conventional IV-based practices rely on fixed empirical thresholds, which lack statistical justification and may be sensitive to characteristics such as class imbalance. In this work, we develop a formal statistical framework for IV by establishing its connection with Jeffreys divergence and propose a novel nonparametric hypothesis test, referred to as the J-Divergence test. Our method provides rigorous asymptotic guarantees and enables interpretable decisions based on \(p\)-values. Numerical experiments, including synthetic and real-world data, demonstrate that the proposed test is more reliable than traditional IV thresholding, particularly under strong imbalance. The test is model-agnostic, computationally efficient, and well-suited for the pre-modeling phase in high-dimensional or imbalanced settings. An open-source Python library is provided for reproducibility and practical adoption. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_13183 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Statistical Hypothesis Testing for Information Value (IV) Rojas, Helder Alvarez, Cirilo Rojas, Nilton Statistics Theory Methodology Machine Learning Information Value (IV) is a widely used technique for feature selection prior to the modeling phase, particularly in credit scoring and related domains. However, conventional IV-based practices rely on fixed empirical thresholds, which lack statistical justification and may be sensitive to characteristics such as class imbalance. In this work, we develop a formal statistical framework for IV by establishing its connection with Jeffreys divergence and propose a novel nonparametric hypothesis test, referred to as the J-Divergence test. Our method provides rigorous asymptotic guarantees and enables interpretable decisions based on \(p\)-values. Numerical experiments, including synthetic and real-world data, demonstrate that the proposed test is more reliable than traditional IV thresholding, particularly under strong imbalance. The test is model-agnostic, computationally efficient, and well-suited for the pre-modeling phase in high-dimensional or imbalanced settings. An open-source Python library is provided for reproducibility and practical adoption. |
| title | Statistical Hypothesis Testing for Information Value (IV) |
| topic | Statistics Theory Methodology Machine Learning |
| url | https://arxiv.org/abs/2309.13183 |