Abstract Number:
2456
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Yang Yang (1), Kai Zhang (2), Ping-Shou Zhong (3)
Institutions:
(1) N/A, N/A, (2) UNC Chapel Hill, N/A, (3) University of Illinois at Chicago, N/A
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
This project focuses on testing conditional independence between two random variables (X and Y) given a set of high-dimensional confounding variables (Z). The high dimensionality of confounding variables poses a challenge for many existing tests, leading to either inflated type-I errors or insufficient power. To address this issue, we leverage the Deep Neural Network (DNN)'s ability to handle complex, high-dimensional data while circumventing the curse of dimensionality. We propose a novel DeepBET test procedure. First, we utilize a DNN model to estimate the conditional means of X and Y given Z using part of the data and obtain predicted errors using the other part of the data. Then, we apply a novel binary expansion statistics to construct our test statistics using predicted errors for dependence detection. Furthermore, we implement a multiple-split
procedure to enhance power, utilizing the entirety of the sample while minimizing randomness. Our results show that the proposed method adeptly controls type I error control and exhibits a significant capacity to detect alternatives, making it a robust approach for testing conditional independence.
Keywords:
Conditional independence|Deep Neural Network|Non-parametric Statistics|Binary Expansion Testing|Multi-split method|
Sponsors:
IMS
Tracks:
Statistical Methodology
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