Statistical Inference for High-Dimensional Models via Recursive Online-Score Estimation.

ABSTRACT: In this paper, we develop a new estimation and valid inference method for single or low-dimensional regression coefficients in high-dimensional generalized linear models. The number of the predictors is allowed to grow exponentially fast with respect to the sample size. The proposed estimator is computed by solving a score function. We recursively conduct model selection to reduce the dimensionality from high to a moderate scale and construct the score equation based on the selected variables. The proposed confidence interval (CI) achieves valid coverage without assuming consistency of the model selection procedure. When the selection consistency is achieved, we show the length of the proposed CI is asymptotically the same as the CI of the "oracle" method which works as well as if the supp

SUBMITTER: Shi C 

PROVIDER: S-EPMC8439566 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature