Efficient estimation in semivarying coefficient models for longitudinal/clustered data

TL;DR

This paper develops an adaptive estimation method for constant coefficients in semivarying coefficient models with longitudinal data, achieving efficiency bounds without assuming known covariance matrices, and demonstrates superior performance through simulations and real data application.

Contribution

It introduces a novel adaptive estimator for constant coefficients in semivarying models that does not require known covariance matrices and attains efficiency bounds.

Findings

Estimator achieves semiparametric efficiency bound under normality.

Proposed method outperforms estimators based on working independence.

Application to CD4 count data reveals new data structure insights.

Abstract

In semivarying coefficient models for longitudinal/clustered data, usually of primary interest is usually the parametric component which involves unknown constant coefficients. First, we study semiparametric efficiency bound for estimation of the constant coefficients in a general setup. It can be achieved by spline regression provided that the within-cluster covariance matrices are all known, which is an unrealistic assumption in reality. Thus, we propose an adaptive estimator of the constant coefficients when the covariance matrices are unknown and depend only on the index random variable, such as time, and when the link function is the identity function. After preliminary estimation, based on working independence and both spline and local linear regression, we estimate the covariance matrices by applying local linear regression to the resulting residuals. Then we employ the covariance matrix estimates and spline regression to obtain our final estimators of the constant coefficients. The proposed estimator achieves the semiparametric efficiency bound under normality assumption, and it has the smallest covariance matrix among a class of estimators even when normality is violated. We also present results of numerical studies. The simulation results demonstrate that our estimator is superior to the one based on working independence. When applied to the CD4 count data, our method identifies an interesting structure that was not found by previous analyses.