{"url":"/method/quanttree","slug":"quanttree","name":"QuantTree","full_name":"QuantTree histograms","full_name_withheld":false,"description_markdown":"Given a training set drawn from an unknown $d$-variate probability distribution, QuantTree constructs a histogram by recursively splitting $\\mathbb{R}^d$. The splits are defined by a stochastic process so that each bin contains a certain proportion of the training set. These histograms can be used to define test statistics (e.g., the Pearson statistic) to tell whether a batch of data is drawn from $\\phi_0$ or not. The most crucial property of QuantTree is that the distribution of any statistic based on QuantTree histograms is independent of $\\phi_0$, thus enabling nonparametric statistical testing.","description_state":"present","introduced_year":null,"introduced_by":{"title":"QuantTree: Histograms for Change Detection in Multivariate Data Streams","paper":"/paper/quanttree-histograms-for-change-detection-in","first_author":"Giacomo Boracchi","n_authors":4,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/quanttree-histograms-for-change-detection-in"},"source":{"url":"https://icml.cc/Conferences/2018/Schedule?showEvent=2268","title":"QuantTree: Histograms for Change Detection in Multivariate Data Streams","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"General","area_id":"general","collection":"Distribution Approximation","url":"/methods/category/distribution-approximation","pwc_aliases":[]}],"n_papers_tagged":3,"archive_num_papers":3,"papers_newest_first":[{"paper":"/paper/class-distribution-monitoring-for-concept","title":"Class Distribution Monitoring for Concept Drift Detection","date":"2022-10-16","arxiv_id":"2210.08470","n_code_links":1,"syntology":null},{"paper":"/paper/nonparametric-and-online-change-detection-in","title":"Nonparametric and Online Change Detection in Multivariate Datastreams using QuantTree","date":"2022-08-30","arxiv_id":"2208.14801","n_code_links":1,"syntology":{"ran":2,"of":2,"unverified":0,"pointer_only":2}},{"paper":"/paper/quanttree-histograms-for-change-detection-in","title":"QuantTree: Histograms for Change Detection in Multivariate Data Streams","date":"2018-07-01","arxiv_id":null,"n_code_links":0,"syntology":null}],"papers_shown":3,"tasks":[{"task":"/task/change-detection","name":"Change Detection","papers":3},{"task":"/task/change-point-detection","name":"Change Point Detection","papers":1},{"task":"/task/drift-detection","name":"Drift Detection","papers":1}],"tasks_shown":3,"n_tasks":3,"usage_by_year":[{"year":"2018","papers":1},{"year":"2022","papers":2}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/quanttree"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}