On high-dimensional Poisson models with measurement error: Hypothesis testing for nonlinear nonconvex optimization
含测量误差的高维泊松模型：非线性非凸优化中的假设检验
We study estimation and testing in the Poisson regression model with noisy high-dimensional covariates, which has wide applications in analyzing noisy big data. Correcting for the estimation bias due to the covariate noise leads to a nonconvex target function to minimize. Treating the high-dimensional issue further leads us to augment an amenable penalty term to the target function. We propose to estimate the regression parameter through minimizing the penalized target…