Smoothing splines via the penalized least squares method provide versatile and effective nonparametric models for regression with Gaussian responses. The computation of smoothing splines is generallyâ€¦ (More)

The (modified) Newton method is adapted to optimize generalized cross validation (GCV) and generalized maximum likelihood (GML) scores with multiple smoothing parameters. The main concerns in solvingâ€¦ (More)

Mixed-effect models are widely used for the analysis of correlated data such as longitudinal data and repeated measures. In this article, we study an approach to the nonparametric estimation ofâ€¦ (More)

Let y i ; i = 1; ; n be independent observations with the density of y i of the form h(y i ; f i) = expy i f i ?b(f i)+c(y i)], where b and c are given functions and b is twice continuouslyâ€¦ (More)

The author proposes some simple diagnostics for the assessment of the necessity of selected model terms in smoothing spline ANOVA models; the elimination of practically insignificant terms generallyâ€¦ (More)

For smoothing parameter selection in penalized likelihood density estimation, a direct crossvalidation strategy is illustrated. The strategy is as effective as the indirect cross-validation developedâ€¦ (More)

Smoothing parameter selection is among the most intensively studied subjects in nonpara-metric function estimation. A closely related issue, that of identifying a proper index for the smoothingâ€¦ (More)

This article extends recent developments in penalized likelihood probability density estimation to the estimation of conditional densities on generic domains. Positivity and unity constraints for aâ€¦ (More)

Penalized likelihood method offers versatile smoothing techniques in a variety of stochastic settings, and the proper selection of the smoothing parameters and other tuning parameters is crucial toâ€¦ (More)

In this article, we propose a penalized clustering method for large scale data with multiple covariates through a functional data approach. In the proposed method, responses and covariates are linkedâ€¦ (More)