Debiased high-dimensional regression calibration for errors-in-variables log-contrast models

Motivated by the challenges in analyzing gut microbiome and metagenomic data, this work aims to tackle the issue of measurement errors in high-dimensional regression models that involve compositional covariates. This paper marks a pioneering effort in conducting statistical inference on high-dimensional compositional data affected by mismeasured or contaminated data. We introduce a calibration approach tailored for the linear log-contrast model. Under relatively lenient conditions regarding the sparsity level of the parameter, we have established the asymptotic normality of the estimator for inference. Numerical experiments and an application in microbiome study have demonstrated the efficacy of our high-dimensional calibration strategy in minimizing bias and achieving the expected coverage rates for confidence intervals. Moreover, the potential application of our proposed methodology extends well beyond compositional data, suggesting its adaptability for a wide range of research contexts.

Bio
Tianying Wang is an Assistant Professor in the Department of Statistics at Colorado State University. She earned her Ph.D. in Statistics from Texas A&M University and completed her postdoctoral training in the Department of Biostatistics at Columbia University. Her research focuses on developing innovative methods and theoretical frameworks in measurement error analysis, quantile regression, and semiparametric and nonparametric approaches, with applications spanning epidemiology, genetics and genomics, and climate science.

Find more of her at: https://tianyingw.github.io