2007/10/01 by Jiming Jiang, Yihui Luan, You-Gan Wang +1 · 23 citations
Mathematics · #Advanced Statistical Methods and Models #Applied mathematics #Asymptotic distribution #Computer science #Convergence (economics) #Estimating equations #Estimator #Iterative method #Least-squares function approximation #Mathematical optimization #Mathematics #Rate of convergence #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #Statistics #math.ST #msc:62F12 #msc:62J02 #msc:65B99 #stat.TH
paper · pdf · doi:10.1214/009053607000000208
published in The Annals of Statistics 35(5) (Institute of Mathematical Statistics) · Published in at http://dx.doi.org/10.1214/009053607000000208 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)
openalex publication_date 2007/10/01 · arxiv created 2007/12/06 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
We propose an iterative estimating equations procedure for analysis of longitudinal data. We show that, under very mild conditions, the probability that the procedure converges at an exponential rate tends to one as the sample size increases to infinity. Furthermore, we show that the limiting estimator is consistent and asymptotically efficient, as expected. The method applies to semiparametric regression models with unspecified covariances among the observations. In the special case of linear models, the procedure reduces to iterative reweighted least squares. Finite sample performance of the procedure is studied by simulations, and compared with other methods. A numerical example from a medical study is considered to illustrate the application of the method.