Hypothesis Testing for Penalized Estimating Equations with Cross-Fitted Covariance Calibration
交叉拟合协方差校准下惩罚估计方程的假设检验
We study hypothesis testing for penalized estimators in settings where the full marginal distribution of a multivariate response is difficult to specify, such as longitudinal data with correlated measurements or high-dimensional heteroscedastic regression. Assuming that the conditional mean model is correctly specified, we establish that the penalized estimating equations admit a $\sqrt{n}$-consistent solution, even when the working covariance structure is misspecified. Our…