In this paper, we consider the generalized semiparametric model (GSPM) y i = h(x T i β) + f (t i) + e i , 1≤ i ≤ n, where h(•) is a known function, e i are dependent errors. We obtain an estimator of the parametric component β for the model by a difference-based M-estimator. In addition, we prove the asymptotic normality of the proposed estimator and investigate the weak convergence rate of the wavelet estimator of f (•). Furthermore, we apply these results to a partially linear model with dependent errors.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.