  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.
