In this short note, we demonstrate the use of principal components analysis (PCA) for one-class support vector machine (one-class SVM) as a dimension reduction tool. However, unlike almost all other usage of PCA which ...

In this article, we propose a model selection and semiparametric estimation method for additive models in the context of quantile regression problems. In particular, we are interested in finding nonzero components as well ...

In this paper, we consider the problem of variable selection for high-dimensional generalized varying-coefficient models and propose a polynomial-spline based procedure that simultaneously eliminates irrelevant predictors ...

Semiparametric models are particularly useful for high-dimensional regression problems. In this paper, we focus on partly linear additive models with a large number of predictors (can be larger than the sample size) and ...

In this short note, we demonstrate that Schwarz’s criterion, which has been used frequently in the literature on quantile regression, is consistent in variable selection. In particular, due to the recent interest in penalized ...

We study the problem of estimating time-varying coefficients in ordinary differential equations. Current theory only applies to the case when the associated state variables are observed without measurement errors as presented ...

In this article, we study the (group) smoothly clipped absolute deviation (SCAD) estimator in the estimation of generalised additive models. The SCAD penalty, proposed by Fan and Li [(2001) ‘Variable Selection via Nonconcave ...

When responses are missing at random, we consider semiparametric estimation of inverse density weighted expectations, or equivalently, integrals of conditional expectations. An inverse probability weighted estimator and a ...

In this article, we consider penalized variable selection in additive Cox models based on (group) smoothly clipped absolute deviation penalty and hence widen the scope of applicability of penalized variable selection to ...