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We combine the global properties of regression splines with the local properties of categorical kernel functions to handle the presence of categorical predictors rather than resorting to sample splitting as is typically done to accommodate their presence. The resulting estimator possesses substantially better finite\u2010sample performance than either its frequency\u2010based peer or cross\u2010validated local linear kernel regression or even additive regression splines (when additivity does not hold). Theoretical underpinnings are provided and Monte Carlo simulations are undertaken to assess finite\u2010sample behavior; and two illustrative applications are provided. An implementation in R is available; see the R package \u2018crs\u2019 for details. Copyright \u00a9 2014 John Wiley &amp; Sons, Ltd.<\/jats:p>","DOI":"10.1002\/jae.2410","type":"journal-article","created":{"date-parts":[[2014,9,2]],"date-time":"2014-09-02T06:27:21Z","timestamp":1409639241000},"page":"705-717","source":"Crossref","is-referenced-by-count":31,"title":["Spline Regression in the Presence of Categorical Predictors"],"prefix":"10.1002","volume":"30","author":[{"given":"Shujie","family":"Ma","sequence":"first","affiliation":[{"name":"Department of Statistics University of California Riverside CA USA"},{"name":"Center for Advanced Statistics and Econometrics Research Soochow University Suzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeffrey S.","family":"Racine","sequence":"additional","affiliation":[{"name":"Department of Economics and Graduate Program in Statistics McMaster University Hamilton Ontario Canada"},{"name":"School of Economics La Trobe University Melbourne Victoria Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lijian","family":"Yang","sequence":"additional","affiliation":[{"name":"Center for Advanced Statistics and Econometrics Research Soochow University Suzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2014,9,2]]},"reference":[{"key":"e_1_2_8_2_1","unstructured":"AbramsonM AudetC CoutureG DennisJ Jr Le DigabelS.2011.The NOMAD project.Technical report GERAD (Groupe d'\u00e9tudes et de recherche en analyse des d\u00e9cisions) Montreal Software available:http:\/\/www.gerad.ca\/nomad[Accessed on 24 August 2014]."},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/63.3.413"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.2307\/2938259"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1573-4412(07)06076-X"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176347963"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1080\/13504850701719629"},{"key":"e_1_2_8_8_1","unstructured":"HallP RacineJS.2013.(forthcoming) Infinite Order Cross\u2010Validated Local Polynomial Regression. 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