Download Penalized Score Test for High Dimensional Logistic Regression

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Department of Statistics
Department of Statistics
The University of Chicago
Penalized Score Test for High Dimensional Logistic Regression
TUESDAY, February 16, 2016, at 9:00 AM
Eckhart 110, 5734 S. University Avenue
We deal with inference problem for high dimensional logistic regression. The main idea is
to give penalized estimator by adding penalty to negative log likelihood function which
penalizes all variables except the one we are interested in. It shows that this penalized
estimator is a compromise between the multiple regression and simple regression. We then
use the algorithm called penalized score test to give valid test for the variable of interest with
asymptotic designed Type I error. We check our algorithm on both simulated and real
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