A Powerful Global Test Statistic for Functional Statistical Inference

We consider the problem of performing an association test between functional data and scalar variables in a varying coefficient model setting. We propose a functional projection regression model and an associated global test statistic to aggregate relatively weak signals across the domain of functional data, while reducing the dimension. An optimal functional projection direction is selected to maximize signal-to-noise ratio with ridge penalty. Theoretically, we systematically study the asymptotic distribution of the global test statistic and provide a strategy to adaptively select the optimal tuning parameter. We use simulations to show that the proposed test outperforms all existing state-of-the-art methods in functional statistical inference. Finally, we apply the proposed testing method to the genome-wide association analysis of imaging genetic data in UK Biobank dataset.

Authors

• Jingwen Zhang Harbin Institute of Technology, Shenzhen
• Joseph Ibrahim
• Tengfei Li
• Hongtu Zhu Departments of Biostatistics, Statistics, Computer Science, and Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, USA

Keywords

No keywords are indexed for this paper.