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Activity Number: 48 - New Frontiers in High-Dimensional and Complex Data analyses
Type: Invited
Date/Time: Sunday, July 28, 2019 : 4:00 PM to 5:50 PM
Sponsor: Biometrics Section
Abstract #300141
Title: Statistical Inference for High-Dimensional Models via Recursive Online-Score Estimation
Author(s): Runze Li*
Companies: Penn State University
Keywords: Confidence interval; Ultrahigh dimensions; Generalized linear models; Online estimation

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 support of the control variables were known. In addition, we prove the proposed CI is asymptotically narrower than the CIs constructed based on the de-sparsified Lasso estimator (van de Geer, et al, 2014) and the decorrelated score statistic (Ning and Liu, 2017). Simulation studies and real data applications are presented to back up our theoretical findings.

Authors who are presenting talks have a * after their name.

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