A Goodness-of-fit Assessment for General Learning Procedure in High Dimensions
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：Liping Zhu (Renmin University of China)
： 2024-11-07 16:30
：Conference Room C503 at Administration Building at Haiyun Campus
Speaker：Liping Zhu (Renmin University of China)
Time：2024-11-7, 16:30
Location：Conference Room C503 at Administration Building at Haiyun Campus
Abstract:
Black-box learners have demonstrated remarkable success across various fields due to their high predictive accuracy. However, the complexity of their learning procedures poses significant challenges in evaluating whether a given learner has achieved optimal performance on datasets with unknown data-generating mechanisms. We propose a general goodness-of-fit test for assessing different learning procedures involving high-dimensional predictors, encompassing methods from classical linear regression to advanced neural networks. Our goodness-of-fit test leverages data-splitting, utilizing the test set to evaluate the black-box learner trained on the training set. This evaluation is based on examining the cumulative covariance of the residuals. Extensive simulations and two real data analyses validate the effectiveness of our method.
