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Estimation for misclassified data with ultra-high levels
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 Title & Authors
Estimation for misclassified data with ultra-high levels
Kang, Moonsu;
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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.
Bayesian;misclassification;multiple imputation;
 Cited by
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