Non parametric regression analysis for longitudinal data with time-depending autoregressive error process

Yin Hang and Shu Liu

Communications in Statistics - Theory and Methods, 2018, vol. 47, issue 18, 4503-4533

Abstract: This paper considers a non parametric longitudinal model, where the within-subject correlation structure is represented by a time-depending autoregressive error process. An initial estimator without taking into account the within-subject correlation is obtained to fit the time-depending autoregressive error process. With the initial estimator, we construct a two-stage local linear estimator of the mean function. According to the asymptotic normality of the initial and two-stage estimators, it is discovered that the two-stage estimator has a smaller asymptotic variance. The simulation results show us that the two-stage estimation has some good properties. The analysis of a data set demonstrates its application.

Date: 2018
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DOI: 10.1080/03610926.2017.1377251

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