Year: 2024
Type: article
Abstract: We consider the complex data modeling problem motivated by the zeroinflated and overdispersed data from microbiome studies.Analyzing how microbiome abundance is associated with human biological features, such as BMI, is of great importance for host health.Methods based on parametric distributional assumptions, such as zero-inflated Poisson and zero-inflated Negative Binomial regression, have been widely used in modeling such data, yet the parametric assumptions are restricted and hard to verify in real-world applications.We relax the parametric assumptions and propose a semiparametric single-index quantile regression model.It is flexible to include a wide range of possible association functions and adaptable to the various zero proportions across subjects, which relaxes the strong parametric distributional assumptions of most existing zero-inflated data modeling approaches.We establish the asymptotic properties for the index coefficients estimator and quantile regression curve estimation.Through extensive simulation studies, we demonstrate the superior performance of the proposed method regarding model fitting.
Source: Statistica Sinica
Authors: Zirui Wang, Tianying Wang
Institutions: Tsinghua University, Colorado State University
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4404574751