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A Consistency Result for Bayes Classifiers with Censored Response Data
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
2013 (English)In: Theoretical mathematics and applications, ISSN 1792-9709, Vol. 3, no 4, 47-54 p.Article in journal (Refereed) Published
Abstract [en]

Naive Bayes classifiers have proven to be useful in many prediction problems with complete training data. Here we consider the situation where a naive Bayes classifier is trained with data where the response is right censored. Such prediction problems are for instance encountered in profiling systems used at National Employment Agencies. In this paper we propose the maximum collective conditional likelihood estimator for the prediction and show that it is strongly consistent under the usual identifiability condition.

Place, publisher, year, edition, pages
2013. Vol. 3, no 4, 47-54 p.
Keyword [en]
Bayesian networks, maximum collective conditional likelihood estimator, strong consistency
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:umu:diva-85225OAI: oai:DiVA.org:umu-85225DiVA: diva2:692266
Available from: 2014-01-30 Created: 2014-01-30 Last updated: 2014-02-25Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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  • Other style
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Language
  • de-DE
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  • en-US
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  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
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