{"doi":"10.7202/1036917ar","title":"Log-Transform Kernel Density Estimation of Income Distribution","abstract":"<jats:p>Standard kernel density estimation methods are very often used in practice to estimate density functions. It works well in numerous cases. However, it is known not to work so well with skewed, multimodal and heavy-tailed distributions. Such features are usual with income distributions, defined over the positive support. In this paper, we show that a preliminary logarithmic transformation of the data, combined with standard kernel density estimation methods, can provide a much better fit of the density estimation.</jats:p>","journal":"L'Actualité économique","year":2016,"id":29246,"datarank":1.5636250150114266,"base_score":3.4011973816621555,"endowment":3.4011973816621555,"self_citation_contribution":0.5101796072493234,"citation_network_contribution":1.0534454077621032,"self_endowment_contribution":0.5101796072493234,"citer_contribution":1.0534454077621032,"corpus_percentile":null,"corpus_rank":null,"citation_count":29,"citer_count":29,"citers_with_citation_signal":24,"citers_with_endowment":24,"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":161742,"name":"Emmanuel Flachaire","orcid":null,"position":1,"is_corresponding":false},{"id":161741,"name":"Arthur Charpentier","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"base_score":3.4011973816621555,"endowment":3.4011973816621555,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"18998881","pmcid":null,"openalex_id":"https://openalex.org/W2144201579","authors":[],"funders":[{"funder_name":"Natural Sciences and Engineering Research Council of Canada","grant_id":"unidentified","title":"unidentified"},{"funder_name":"French National Research Agency (ANR)","grant_id":"ANR-11-IDEX-0001","title":null}],"total_grants":2,"fwci":2.5364,"citation_percentile":0.89327136,"influential_citations":4,"citation_trend":[{"year":2018,"count":5},{"year":2019,"count":2},{"year":2020,"count":6},{"year":2021,"count":5},{"year":2022,"count":5},{"year":2023,"count":1},{"year":2025,"count":2},{"year":2026,"count":2}],"oa_status":"bronze","license":null,"oa_locations":[{"url":"http://www.erudit.org/fr/revues/ae/2015-v91-n1-2-ae02507/1036917ar.pdf","host_type":"journal"},{"url":"http://www.amse-aixmarseille.fr/sites/default/files/_dt/2012/wp_2015_-_nr_06.pdf","host_type":"GREEN"},{"url":"http://www.erudit.org/fr/revues/ae/2015-v91-n1-2-ae02507/1036917ar.pdf","host_type":"publisher"},{"url":"https://doi.org/10.7202/1036917ar","host_type":"journal"},{"url":"https://amu.hal.science/hal-01457340","host_type":"repository"},{"url":"http://id.erudit.org/iderudit/1036917ar","host_type":"repository"},{"url":"https://doi.org/10.2139/ssrn.2514882","host_type":""},{"url":"https://amu.hal.science/hal-01457340v1","host_type":""},{"url":"https://dx.doi.org/10.7202/1036917ar","host_type":""},{"url":"https://dx.doi.org/10.2139/ssrn.2514882","host_type":""},{"url":"https://id.erudit.org/iderudit/1036917ar","host_type":""},{"url":"https://halshs.archives-ouvertes.fr/halshs-01115988/document","host_type":""},{"url":"https://doi.org/https://doi.org/10.7202/1036917ar","host_type":""}],"fields_of_study":["Statistical Methods and Inference","Monetary Policy and Economic Impact","Financial Risk and Volatility Modeling","Mathematics","Economics","05 social sciences","01 natural sciences","jel:C15","0502 economics and business","0101 mathematics"],"mesh_terms":[],"keywords":["Kernel density estimation","Multivariate kernel density estimation","Density estimation","Variable kernel density estimation","Kernel (algebra)","Transformation (genetics)","Mathematics","Estimation","Distribution (mathematics)","Logarithm","Kernel method","Statistics","Applied mathematics","Computer science","Artificial intelligence","Mathematical analysis","Economics","Support vector machine","Discrete mathematics","lognormal kernel,data transformation,income distribution,heavy-tail,nonparametric density estimation","[SHS.ECO]Humanities and Social Sciences/Economics and Finance","[SHS]Humanities and Social Sciences","nonparametric density estimation, heavy-tail, income distribution, data transformation, lognormal kernel","[SHS] Humanities and Social Sciences","[SHS.ECO] Humanities and Social Sciences/Economics and Finance","Economie quantitative"],"sdg_mappings":[{"sdg_number":1,"sdg_label":"1. 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