Skip to main content
Skip to main menu Skip to spotlight region Skip to secondary region Skip to UGA region Skip to Tertiary region Skip to Quaternary region Skip to unit footer


Yi Li

<a href="">University of Michigan</a>

We propose a new class of estimating equation-based Dantzig selectors that can achieve simultaneous estimation and variable selection in the absence of a likelihood function, even when the number of covariates exceeds the number of samples. Our research was motivated by practical problems encountered in two studies: a clinical trial of therapies for head and neck cancer, and a genomics study of multiple myeloma patients. These problems proved difficult to analyze under the likelihood setting and must instead be approached with estimating equations. We prove nonasymptotic probability bounds on the accuracy of our estimator, report extensive simulation results, and use our method to analyze the aforementioned problems and construct more accurate prediction rules. (Joint work with Dave Zhao from Harvard University)

Support us

We appreciate your financial support. Your gift is important to us and helps support critical opportunities for students and faculty alike, including lectures, travel support, and any number of educational events that augment the classroom experience. Click here to learn more about giving.

Every dollar given has a direct impact upon our students and faculty.