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dc.contributor.authorMadden, Gary
dc.contributor.authorApergis, Nicholas
dc.contributor.authorRappoport, Paul
dc.contributor.authorBanerjee, Anniruddha
dc.date.accessioned2019-01-15T14:57:40Z
dc.date.available2019-01-15T14:57:40Z
dc.date.issued2017-09-01
dc.identifier.citationMadden, G. et al. (2018) ‘An application of nonparametric regression to missing data in large market surveys’, Journal of Applied Statistics, 45(7), pp. 1292-1302, DOI: 10.1080/02664763.2017.1369498en
dc.identifier.issn0266-4763
dc.identifier.doi10.1080/02664763.2017.1369498
dc.identifier.urihttp://hdl.handle.net/10545/623301
dc.description.abstractNon-response (or missing data) is often encountered in large-scale surveys. To enable the behavioural analysis of these data sets, statistical treatments are commonly applied to complete or remove these data. However, the correctness of such procedures critically depends on the nature of the underlying missingness generation process. Clearly, the efficacy of applying either case deletion or imputation procedures rests on the unknown missingness generation mechanism. The contribution of this paper is twofold. The study is the first to propose a simple sequential method to attempt to identify the form of missingness. Second, the effectiveness of the tests is assessed by generating (experimentally) nine missing data sets by imposed MCAR, MAR and NMAR processes, with data removed.
dc.description.sponsorshipN/Aen
dc.language.isoenen
dc.publisherTaylor & Francisen
dc.relation.urlhttps://www.tandfonline.com/doi/full/10.1080/02664763.2017.1369498en
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectTreatment of missing dataen
dc.subjectSurvey samplingen
dc.subjectUnited Statesen
dc.subjectConsumer preferencesen
dc.titleAn application of nonparametric regression to missing data in large market surveysen
dc.typeArticleen
dc.identifier.eissn1360-0532
dc.contributor.departmentCurtin Universityen
dc.contributor.departmentUniversity of Piraeusen
dc.contributor.departmentTemple Universityen
dc.contributor.departmentAdvance Analyticsen
dc.identifier.journalJournal of Applied Statisticsen
dc.dateAccepted2017-08-13
dc.dateAccepted2017-08-13
html.description.abstractNon-response (or missing data) is often encountered in large-scale surveys. To enable the behavioural analysis of these data sets, statistical treatments are commonly applied to complete or remove these data. However, the correctness of such procedures critically depends on the nature of the underlying missingness generation process. Clearly, the efficacy of applying either case deletion or imputation procedures rests on the unknown missingness generation mechanism. The contribution of this paper is twofold. The study is the first to propose a simple sequential method to attempt to identify the form of missingness. Second, the effectiveness of the tests is assessed by generating (experimentally) nine missing data sets by imposed MCAR, MAR and NMAR processes, with data removed.


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