In this paper, we study a new method for function approximation from the given integral values over successive subintervals by using cubic B-splines. The method does not need any additional end conditions and it is easy to be implemented without solving any system of linear equations. The method...

Functional linear regression has been widely used to model the relationship between a scalar response and functional predictors. If the original data do not satisfy the linear assumption, an intuitive solution is to perform some transformation such that transformed data will be linearly related....

In this paper, we apply the group smoothly clipped absolute deviation (SCAD) penalty to identify the model structure of the semiparametric varying coefficient partially linear model. The performance of the new approach is demonstrated in terms of the theoretical and numerical results.

We propose a fully Bayesian inference for semiparametric joint mean and variance models on the basis of B-spline approximations of nonparametric components. An efficient MCMC method which combines Gibbs sampler and Metropolis–Hastings algorithm is suggested for the inference, and the...

The package bspline, downloadable from Statistical Software Components, now has three commands. The first, bspline, generates a basis of Schoenberg B-splines. The second, frencurv, generates a basis of reference splines whose parameters in the regression model are simply values of the spline at...

This note deals with the study of a functional linear model for time series prediction which combines a functional endogenous predictor and real and functional exogenous variables. A penalized B-spline type estimator for the real and functional coefficients is presented and some weak consistency...

In this paper, we propose a robust empirical likelihood (REL) inference for the parametric component in a generalized partial linear model (GPLM) with longitudinal data. We make use of bounded scores and leverage-based weights in the auxiliary random vectors to achieve robustness against...

In this paper, we propose a two-stage variable selection procedure for high dimensional quantile varying coefficient models. The proposed method is based on basis function approximation and LASSO-type penalties. We show that the first stage penalized estimator with LASSO penalty reduces the...

A semiparametric copula model for bivariate survival data is characterized by a parametric copula model of dependence and nonparametric models of two marginal survival functions. Efficient estimation for the semiparametric copula model has been recently studied for the complete data case. When...