Multiple-Instance Logistic Regression with LASSO Penalty
  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. 
Ray-Bing Chen, Kuang-Hung Cheng, Sheng-Mao Chang, Shuen-Lin Jeng,   Ping-Yang Chen, Chun-Hao Yang, and Chi-Chun Hsia
stat.ML stat.AP