Multiple-Instance Logistic Regression with LASSO Penalty
This work addresses a domain-specific problem in manufactory process modeling, offering an incremental improvement by extending existing methods with penalty terms.
The authors tackled the problem of estimating coefficients in a multiple-instance logistic regression model for a manufactory process, proposing an expectation-maximization algorithm with LASSO penalty to identify important covariates, and demonstrated its usefulness through simulations and real examples.
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.