We develop an interquantile shrinkage estimation method to examine the underlying commonality structure of regression coefficients across various quantile levels for longitudinal data in a data-driven manner.This meth...We develop an interquantile shrinkage estimation method to examine the underlying commonality structure of regression coefficients across various quantile levels for longitudinal data in a data-driven manner.This method provides a deeper insight into the relationship between the response and covariates,leading to enhanced estimation efficiency and model interpretability.We propose a fused penalized generalized estimation equation(GEE)estimator with a non-crossing constraint,which automatically promotes constancy in estimates across neighboring quantiles.By accounting for within-subject correlation in longitudinal data,the GEE estimator improves estimation efficiency.We employ a nested alternating direction method of multiplier(ADMM)algorithm to minimize the regularized objective function.The asymptotic properties of the penalized estimators are established.Furthermore,in the presence of irrelevant predictors,we develop a doubly penalized GEE estimator to simultaneously select active variables and identify commonality across quantiles.Numerical studies demonstrate the superior performance of our proposed methods in terms of estimation efficiency.We illustrate the application of our methodologies by analyzing a longitudinal wage dataset.展开更多
基金supported by National Key R&D Program of China(Grant No.2022YFA1003800)National Natural Science Foundation of China(Grant Nos.12301344,12471265,12231011 and 71988101)the Research Grant Council,University Grant Committee of Hong Kong Special Administrative Region(Grant No.14303622)。
文摘We develop an interquantile shrinkage estimation method to examine the underlying commonality structure of regression coefficients across various quantile levels for longitudinal data in a data-driven manner.This method provides a deeper insight into the relationship between the response and covariates,leading to enhanced estimation efficiency and model interpretability.We propose a fused penalized generalized estimation equation(GEE)estimator with a non-crossing constraint,which automatically promotes constancy in estimates across neighboring quantiles.By accounting for within-subject correlation in longitudinal data,the GEE estimator improves estimation efficiency.We employ a nested alternating direction method of multiplier(ADMM)algorithm to minimize the regularized objective function.The asymptotic properties of the penalized estimators are established.Furthermore,in the presence of irrelevant predictors,we develop a doubly penalized GEE estimator to simultaneously select active variables and identify commonality across quantiles.Numerical studies demonstrate the superior performance of our proposed methods in terms of estimation efficiency.We illustrate the application of our methodologies by analyzing a longitudinal wage dataset.
