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We approximate the empiric distribution function, rather than the true distribution function. This enables us to use trigonometric series methods to estimate distribution functions with unbounded support.<\/jats:p>","DOI":"10.1111\/j.2517-6161.1983.tb01234.x","type":"journal-article","created":{"date-parts":[[2018,12,7]],"date-time":"2018-12-07T06:45:41Z","timestamp":1544165141000},"page":"81-88","source":"Crossref","is-referenced-by-count":13,"title":["Orthogonal Series Distribution Function Estimation, with Applications"],"prefix":"10.1093","volume":"45","author":[{"given":"Peter","family":"Hall","sequence":"first","affiliation":[{"name":"Australian National University , Canberra , Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2018,12,5]]},"reference":[{"key":"2024090920521551100_rssb01234-cit-0001","volume-title":"Handbook of Mathematical Functions.","author":"Abramowitz","year":"1965"},{"key":"2024090920521551100_rssb01234-cit-0002","first-page":"427","article-title":"A strong approximation for the multivariate empirical process","volume":"10","author":"Cs\u00f6rg\u0151","year":"1975","journal-title":"Studia Sci. 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