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Register a new userMultiple-Instance Logistic Regression with LASSO Penalty
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Added by
Sheng-Mao Chang
Publication date
2016
fields
Mathematical Statistics
and research's language is
English
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In this work, we consider a manufactory process which can be described by a multiple-instance logistic regression model. In order to compute the maximum likelihood estimation of the unknown coefficient, an expectation-maximization algorithm is proposed, and the proposed modeling approach can be extended to identify the important covariates by adding the coefficient penalty term into the likelihood function. In addition to essential technical details, we demonstrate the usefulness of the proposed method by simulations and real examples.
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