Two-stage efficient estimation of longitudinal nonparametric additive models

• Published in: Statistics & probability letters Vol. 77; no. 17; pp. 1666 - 1675
• Main Authors: You, Jinhong, Zhou, Haibo
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.11.2007; Elsevier
• Series: Statistics & Probability Letters
• Subjects: Additive model; Distribution theory; Efficiency; Exact sciences and technology; General topics; Longitudinal data; Longitudinal data Additive model Two-stage estimation Efficiency Oracle property; Mathematics; Nonparametric inference; Oracle property; Probability and statistics; Probability theory and stochastic processes; Sciences and techniques of general use; Statistics; Two-stage estimation; secondary 62A10; Efficiency; Oracle property; Two-stage estimation; Longitudinal data; primary 62H12; Additive model; Smoothing methods; Probability theory; Asymptotic behavior; Finite sample; Nonparametric regression; Non parametric estimation; Statistical estimation; Statistical simulation; primary 62H12; secondary 62A10; Statistical method; Estimator efficiency; Smoothing; Regression model; Asymptotic normality; Longitudinal data; Additive model; Two-stage estimation; Efficiency; Oracle property

Nonparametric smoothings are useful tool to model longitudinal data. In this paper we study the estimating problem of longitudinal nonparametric additive regression models. A two-stage efficient approach is developed to estimate the unknown additive components. We show the resulted estimators have s...