Multiple-Instance Logistic Regression with LASSO Penalty

2016/07/13 by Ray‐Bing Chen, Kuang-Hung Cheng, Chen, Ray-Bing +11
Chemistry · Decision Sciences · Mathematics · #Advanced Statistical Methods and Models #Advanced Statistical Process Monitoring #Applications (stat.AP) #FOS: Computer and information sciences #Machine Learning (stat.ML) #Spectroscopy and Chemometric Analyses

paper · pdf · doi:10.48550/arxiv.1607.03615

openalex publication_date 2016/07/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Bias reduction of maximum likelihood estimates
• Penalized Regressions: The Bridge versus the Lasso
• Solving the multiple instance problem with axis-parallel rectangles

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