A1266
Title: Debiased high-dimensional regression calibration for errors-in-variables log-contrast models
Authors: Tianying Wang - Colorado State University (United States) [presenting]
Abstract: Motivated by the challenges in analyzing gut microbiome and metagenomic data, the aim is to tackle the issue of measurement errors in high-dimensional regression models that involve compositional covariates. A pioneering effort is made to conduct statistical inference on high-dimensional compositional data affected by mismeasured or contaminated data. A calibration approach tailored to the linear log-contrast model is introduced. Under relatively lenient conditions regarding the sparsity level of the parameter, the asymptotic normality of the estimator for inference is established. Numerical experiments and an application in microbiome study have demonstrated the efficacy of the high-dimensional calibration strategy in minimizing bias and achieving the expected coverage rates for confidence intervals. Moreover, the potential application of the proposed methodology extends well beyond compositional data, suggesting its adaptability for a wide range of research contexts.
