تقرير
Density Ratio Estimation and Neyman Pearson Classification with Missing Data
| العنوان: | Density Ratio Estimation and Neyman Pearson Classification with Missing Data |
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| المؤلفون: | Givens, Josh, Liu, Song, Reeve, Henry W J |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Statistics - Machine Learning, Computer Science - Machine Learning, Statistics - Methodology |
| الوصف: | Density Ratio Estimation (DRE) is an important machine learning technique with many downstream applications. We consider the challenge of DRE with missing not at random (MNAR) data. In this setting, we show that using standard DRE methods leads to biased results while our proposal (M-KLIEP), an adaptation of the popular DRE procedure KLIEP, restores consistency. Moreover, we provide finite sample estimation error bounds for M-KLIEP, which demonstrate minimax optimality with respect to both sample size and worst-case missingness. We then adapt an important downstream application of DRE, Neyman-Pearson (NP) classification, to this MNAR setting. Our procedure both controls Type I error and achieves high power, with high probability. Finally, we demonstrate promising empirical performance both synthetic data and real-world data with simulated missingness. Comment: 40 pages, 11 Figures. To be published in proceedings for AISTAT 2023 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2302.10655 |
| رقم الأكسشن: | edsarx.2302.10655 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |