{"doi":"10.1214/aos/1176344552","title":"Bootstrap Methods: Another Look at the Jackknife","abstract":"We discuss the following problem: given a random sample $\\\\mathbf{X} = (X_1, X_2, \\\\cdots, X_n)$ from an unknown probability distribution $F$, estimate the sampling distribution of some prespecified random variable $R(\\\\mathbf{X}, F)$, on the basis of the observed data $\\\\mathbf{x}$. (Standard jackknife theory gives an approximate mean and variance in the case $R(\\\\mathbf{X}, F) = \\\\theta(\\\\hat{F}) - \\\\theta(F), \\\\theta$ some parameter of interest.) A general method, called the \"bootstrap,\" is introduced, and shown to work satisfactorily on a variety of estimation problems. The jackknife is shown to be a linear approximation method for the bootstrap. The exposition proceeds by a series of examples: variance of the sample median, error rates in a linear discriminant analysis, ratio estimation, estimating regression parameters, etc.","journal":"The Annals of Statistics","year":1979,"id":3996,"datarank":43.19197965081908,"base_score":9.759905824058103,"endowment":9.759905824058103,"self_citation_contribution":1.4639858736087157,"citation_network_contribution":41.727993777210365,"self_endowment_contribution":1.4639858736087157,"citer_contribution":41.727993777210365,"corpus_percentile":99.9,"corpus_rank":569,"citation_count":17324,"citer_count":199,"citers_with_citation_signal":199,"citers_with_endowment":199,"datacite_reuse_total":0,"is_dataset":false,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"1979-01-01","authors":[{"id":40073,"name":"B. Efron","orcid":null,"position":0,"is_corresponding":true}],"reference_count":14,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-03-01T18:20:47.508186Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}