科研之友
微信
新浪微博
Facebook
分享链接
摘要
Consider the semiparametric regression model y(i) = x(i)(T)beta+g(t(i))+epsilon(i) for i = 1,..., n, where x(1) is an element of R-p are the random design vectors, t(i) are the constant sequences on [0, 1], beta is an element of R-p is an unknown vector of the slop parameter, g is an unknown real-valued function defined on the closed interval [0, 1], and the error random variables epsilon(i), are coming from a stationary stochastic process, satisfying the strong mixing condition in some results. Under suitable conditions, we obtain expansions for the bias and the variance of wavelet estimators (beta) over cap (n) and (g) over cap (n) (.) of beta and g (.) respectively, prove their weak consistency, and establish the asymptotic normality and the Berry-sseen bound of (beta) over cap (n).
