Z. Q. Cai

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In this article we study nonparametric estimation of regression quantiles by inverting a weighted Nadaraya-Watson estimator (WNW) of conditional distribution function, which was rst used by Hall, Woll and Yao (1999). First, under some regularity conditions, we establish the asymptotic normality and weak consistency of the WNW conditional distribution(More)
In this paper, we analyze the biochemical oxygen demand data collected over two years from by tting an autoregressive model with time-dependent coeecients. The local linear smoothing technique is developed and implemented to estimate the coeecient functions of the autoregressive model. A nonparametric version of the Akaike information criterion is developed(More)
In this paper, quantile regression methods are suggested for a class of smooth coefficient time series models. We employ a local linear fitting scheme to estimate the smooth coefficients in the quantile framework. The programming involved in the local linear quantile estimation is relatively simple and it can be modified with few efforts from the existing(More)
In this article we study nonparametric estimation of regression function by using the weighted Nadaraya-Watson approach. We establish the asymptotic normality and weak consistency of the resulting estimator for-mixing time series at both boundary and interior points, and we show that the estimator preserves the bias, variance, and more importantly,(More)