{"doi":"10.1111/rssb.12019","title":"Outlier Robust Small Area Estimation","abstract":"<jats:title>Summary</jats:title><jats:p>Recently proposed outlier robust small area estimators can be substantially biased when outliers are drawn from a distribution that has a different mean from that of the rest of the survey data. This naturally leads one to consider an outlier robust bias correction for these estimators. We develop this idea, proposing two different analytical mean-squared error estimators for the ensuing bias-corrected outlier robust estimators. Simulations based on realistic outlier-contaminated data show that the bias correction proposed often leads to more efficient estimators. Furthermore, the mean-squared error estimation methods proposed appear to perform well with a variety of outlier robust small area estimators.</jats:p>","journal":"Journal of the Royal Statistical Society Series B: Statistical Methodology","year":2014,"id":642144,"datarank":0.7156026936698499,"base_score":4.770684624465665,"endowment":4.770684624465665,"self_citation_contribution":0.7156026936698499,"citation_network_contribution":0.0,"self_endowment_contribution":0.7156026936698499,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":117,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1670018,"name":"Hukum Chandra","orcid":null,"position":1,"is_corresponding":false},{"id":1670020,"name":"Nicola Salvati","orcid":null,"position":2,"is_corresponding":false},{"id":1670022,"name":"Nikos Tzavidis","orcid":null,"position":3,"is_corresponding":false},{"id":1670017,"name":"Ray Chambers","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Outlier Robust Small Area Estimation","abstract":"<jats:title>Summary</jats:title><jats:p>Recently proposed outlier robust small area estimators can be substantially biased when outliers are drawn from a distribution that has a different mean from that of the rest of the survey data. This naturally leads one to consider an outlier robust bias correction for these estimators. We develop this idea, proposing two different analytical mean-squared error estimators for the ensuing bias-corrected outlier robust estimators. Simulations based on realistic outlier-contaminated data show that the bias correction proposed often leads to more efficient estimators. Furthermore, the mean-squared error estimation methods proposed appear to perform well with a variety of outlier robust small area estimators.</jats:p>","is_dataset_classified":null,"base_score":4.770684624465665,"endowment":4.770684624465665,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19910364","pmcid":null,"openalex_id":"https://openalex.org/W2107646698","authors":[],"funders":[{"funder_name":"Small area methods for poverty and living condition estimates","grant_id":"SSH-CT-2007-217565","title":null},{"funder_name":"Small area methods for poverty and living condition estimates","grant_id":"FP7-SSH-2007-1","title":null},{"funder_name":"Australian Research Council","grant_id":"LP0776810","title":null}],"total_grants":3,"fwci":10.9826,"citation_percentile":0.98856932,"influential_citations":0,"citation_trend":[{"year":2012,"count":2},{"year":2013,"count":7},{"year":2014,"count":5},{"year":2015,"count":14},{"year":2016,"count":11},{"year":2017,"count":7},{"year":2018,"count":10},{"year":2019,"count":11},{"year":2020,"count":12},{"year":2021,"count":3},{"year":2022,"count":9},{"year":2023,"count":6},{"year":2024,"count":2},{"year":2025,"count":10},{"year":2026,"count":7}],"oa_status":"closed","license":"https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model","oa_locations":[{"url":"https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Frssb.12019","host_type":"publisher"},{"url":"https://academic.oup.com/jrsssb/article-pdf/76/1/47/49514322/jrsssb_76_1_47.pdf","host_type":"publisher"},{"url":"https://doi.org/10.1111/rssb.12019","host_type":"journal"},{"url":"https://eprints.soton.ac.uk/181955/","host_type":"repository"}],"fields_of_study":["Advanced Statistical Methods and Models","Spatial and Panel Data Analysis","Statistical Methods and Inference"],"mesh_terms":[],"keywords":["Outlier","Estimator","Robust statistics","Mean squared error","Statistics","Anomaly detection","M-estimator","Computer science","Mathematics","Data mining"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-07T21:42:26.510693Z","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,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}