We propose a global smoothing method based on polynomial splines for the estimation of
functional coefficient regression models for non‐linear time series. Consistency and rate of …

Motivated by two practical problems, we propose a new procedure for estimating a
semivarying‐coefficient model. Asymptotic properties are established which show that the …

This paper offers a new approach for estimating and forecasting the volatility of financial time
series. No assumption is made about the parametric form of the processes. On the contrary …

Smoothing spline estimators are considered for inference in varying-coefficient models with
one effect modifying covariate. Bayesian 'confidence intervals' are developed for the …

This paper deals with the estimation of conditional quantiles in varying coefficient models by
estimating the coefficients. Varying coefficient models are among popular models that have …

This paper is concerned with the estimation of a varying-coefficient partially linear
regression model that is frequently used in statistical modeling. We first construct estimators …

Generalized likelihood ratio statistics have been proposed in Fan, Zhang and Zhang [Ann.
Statist. 29 (2001) 153–193] as a generally applicable method for testing nonparametric …

We consider the problem of multistep-ahead prediction in time series analysis by using
nonparametric smoothing techniques. Forecasting is always one of the main objectives in …

We propose a semiparametric multilevel survival model for clustered duration data in which
the effect of a continuous covariate is represented by an unspecified, possibly non-linear …

The paper presents a unified approach to local likelihood estimation for a broad class of
nonparametric models, including eg the regression, density, Poisson and binary response …