A Sieve Quasi-likelihood Ratio Test for Neural Networks with Applications to Genetic Association Studies

• URL: http://arxiv.org/abs/2212.08255v1
• Date: Fri, 16 Dec 2022 02:54:46 GMT
• Title: A Sieve Quasi-likelihood Ratio Test for Neural Networks with Applications to Genetic Association Studies
• Authors: Xiaoxi Shen, Chang Jiang, Lyudmila Sakhanenko and Qing Lu
• Abstract summary: We propose a quasi sieve-likelihood ratio test based on NN with one hidden layer for testing complex associations. The validity of the distribution is investigated via simulations. We demonstrate the use of the proposed test by performing a genetic association analysis of the sequencing data from Alzheimer's Disease Neuroimaging Initiative (ADNI)
• Score: 0.32771631221674324
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural networks (NN) play a central role in modern Artificial intelligence (AI) technology and has been successfully used in areas such as natural language processing and image recognition. While majority of NN applications focus on prediction and classification, there are increasing interests in studying statistical inference of neural networks. The study of NN statistical inference can enhance our understanding of NN statistical proprieties. Moreover, it can facilitate the NN-based hypothesis testing that can be applied to hypothesis-driven clinical and biomedical research. In this paper, we propose a sieve quasi-likelihood ratio test based on NN with one hidden layer for testing complex associations. The test statistic has asymptotic chi-squared distribution, and therefore it is computationally efficient and easy for implementation in real data analysis. The validity of the asymptotic distribution is investigated via simulations. Finally, we demonstrate the use of the proposed test by performing a genetic association analysis of the sequencing data from Alzheimer's Disease Neuroimaging Initiative (ADNI).

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