{"url":"/dataset/ml-for-twosampletesting","name":"ML_for_TwoSampleTesting","full_name":null,"description_markdown":"# Machine Learning for Two-Sample Testing under Right-Censored Data: A Simulation Study\r\n- Petr PHILONENKO, Ph.D. in Computer Science;\r\n\r\n- Sergey POSTOVALOV, D.Sc. in Computer Science.\r\n\r\nThe paper can be downloaded [here](https://arxiv.org/abs/2409.08201).\r\n\r\n# About\r\nThis dataset is a supplement to the github repositiry and paper addressed to solve the two-sample problem under right-censored observations using Machine Learning. The problem statement can be formualted as H0: S1(t)=S2(t) versus H: S1(t)≠S_2(t) where S1(t) and S2(t) are survival functions of samples X1 and X2.\r\n\r\nThis dataset contains the synthetic data simulated by the Monte Carlo method and Inverse Transform Sampling.","description_withheld":null,"homepage":"https://huggingface.co/datasets/pfilonenko/ML_for_TwoSampleTesting","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":{"name":"Apache 2.0","url":null},"modalities":[],"tasks":[],"languages":[],"variants":["ML_for_TwoSampleTesting"],"data_loaders":[],"num_papers_in_archive":0,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}