{"url":"/dataset/ml-for-two-sample-testing","name":"ML for Two-Sample Testing","full_name":null,"description_markdown":"This dataset is a supplement to the github repositiry (https://github.com/pfilonenko/ML_for_TwoSampleTesting) 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":"2024-09-12","introduced_date_note":null,"introduced_by":{"paper":"/paper/machine-learning-for-two-sample-testing-under","title":"Machine Learning for Two-Sample Testing under Right-Censored Data: A Simulation Study","first_author":"Petr Philonenko","url":null},"license":{"name":"Apache-2.0","url":null},"modalities":[],"tasks":[],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["ML for Two-Sample Testing"],"data_loaders":[],"num_papers_in_archive":1,"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."}