Conference paper

Consistency of $\ell_1$-Regularized Maximum-Likelihood for Compressive Poisson Regression

We consider Poisson regression with the canonical link function. This regression model is widely used in regression analysis involving count data; one important application in electrical engineering is transmission tomography. In this paper, we establish the variable selection consistency and estimation consistency of the $\ell_1$-regularized maximum-likelihood estimator in this regression model, and characterize the asymptotic sample complexity that ensures consistency even under the compressive sensing setting (or the $n \ll p$ setting in high-dimensional statistics).


    • EPFL-CONF-205082

    Record created on 2015-02-16, modified on 2017-05-10

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