Summary of Machine Learning For Two-sample Testing Under Right-censored Data: a Simulation Study, by Petr Philonenko and Sergey Postovalov
Machine Learning for Two-Sample Testing under Right-Censored Data: A Simulation Study
by Petr Philonenko, Sergey Postovalov
First submitted to arxiv on: 12 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation (stat.CO); Methodology (stat.ME); Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study aims to assess the efficacy of Machine Learning (ML) approaches for two-sample testing with right-censored observations. The researchers develop several ML-based methods, each combining predictions from classical two-sample tests, and evaluate their statistical power, null distributions, and feature significance. This work encompasses 18 methods, including proposed and well-studied two-sample tests, and uses a synthetic dataset generated via inverse transform sampling and replicated through Monte Carlo simulation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study evaluates the effectiveness of Machine Learning (ML) methods for two-sample testing with right-censored observations. By developing new ML-based approaches and comparing them to classical methods, researchers aim to improve statistical power and understanding of feature significance. The results are based on a synthetic dataset generated using the inverse transform sampling method and replicated multiple times through Monte Carlo simulation. |
Keywords
» Artificial intelligence » Machine learning
