Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
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Estimation for misclassified data with ultra-high levels

  • Kang, Moonsu (Department of Information Statistics, Gangneung-Wonju National University)
  • Received : 2015.08.04
  • Accepted : 2016.01.15
  • Published : 2016.01.31
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Abstract

Outcome misclassification is widespread in classification problems, but methods to account for it are rarely used. In this paper, the problem of inference with misclassified multinomial logit data with a large number of multinomial parameters is addressed. We have had a significant swell of interest in the development of novel methods to infer misclassified data. One simulation study is shown regarding how seriously misclassification issue occurs if the number of categories increase. Then, using the group lasso regression, we will show how the best model should be fitted for that kind of multinomial regression problems comprehensively.

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References

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