This note variable selection in the semiparametric linear regression model for censored data. Semiparametric linear regression for censored data is a natural extension of the linear model for uncensored data; however, random censoring introduces substantial theoretical and numerical challenges. By now, a number of authors have made significant contributions for estimation and inference in the s...

The problems of dimension reduction and inference of statistical dependence are addressed by the modeling framework of learning gradients. The models we propose hold for Euclidean spaces as well as the manifold setting. The central quantity in this approach is an estimate of the gradient of the regression or classification function. Two quadratic forms are constructed from gradient estimates: t...

‎in this paper‎, ‎some kkt type sufficient global optimality conditions‎ ‎for general mixed integer nonlinear programming problems with‎ ‎equality and inequality constraints (minpp) are established‎. ‎we achieve‎ ‎this by employing a lagrange function for minpp‎. ‎in addition‎, ‎verifiable sufficient global optimality conditions for general mixed‎ ‎integer quadratic programming problems are der...

Most panel method implementations use both low order basis function representations of the solution and flat panel representations of the body surface. Although several implementations of higher order panel methods exist, difficulties in robustly computing the self term integrals remain. In this paper, methods for integrating the single and double layer self term integrals are presented. The ap...

A simple linear averaging of the outputs of several networks as e.g. in bagging 3], seems to follow naturally from a bias/variance decomposition of the sum-squared error. The sum-squared error of the average model is a quadratic function of the weighting factors assigned to the networks in the ensemble 7], suggesting a quadratic programmingalgorithm for nding the \optimal"weighting factors. If ...

In this paper we present an algorithm of quasi-linear complexity for exactly calculating the infimal convolution of convex quadratic functions. The algorithm exactly and simultaneously solves a separable uniparametric family of quadratic programming problems resulting from varying the equality constraint.

In this paper, we consider minimizing the ratio of two indefinite quadratic functions subject to two quadratic constraints. Using the extension of Charnes– Cooper transformation, we transform the problem to a homogenized quadratic problem. Then, we show that, under certain assumptions, it can be solved to global optimality using semidefinite optimization relaxation.