We propose a sharp change point estimator based on the differences between right and left boundary wavelet smoothers. It is constructed by applying a two-step procedure to the observed data and has the minimax convergence rate. Next, we estimate the regression function with boundary wavelets in...

The spherical deconvolution problem was first proposed by Rooij and Ruymgaart (in: G. Roussas (Ed.), Nonparametric Functional Estimation and Related Topics, Kluwer Academic Publishers, Dordrecht, 1991, pp. 679-690) and subsequently solved in Healy et al. (J. Multivariate Anal. 67 (1998) 1). Kim...

The support vector machine has been a popular choice of classification method for many applications in machine learning. While it often outperforms other methods in terms of classification accuracy, the implicit nature of its solution renders the support vector machine less attractive in...

This paper examines the estimation of an indirect signal embedded in white noise on vector bundles. It is found that the sharp asymptotic minimax bound is determined by the degree to which the indirect signal is embedded in the linear operator. Thus when the linear operator has polynomial decay,...

This paper proposes a new B-spline method for nonparametric regression function estimation which can be applied to the case even when the covariate is contaminated with noise. A property of B-splines, reproducing line property, is crucial in the construction of B-spline estimators for regression...

Microarray experiments have raised challenging questions such as how to make an accurate identification of a set of marker genes responsible for various cancers. In statistics, this specific task can be posed as the feature selection problem. Since a support vector machine can deal with a vast...