Zobrazeno 1 - 10
of 143
pro vyhledávání: '"Botev, Zdravko"'
We present a new optimization method for the group selection problem in linear regression. In this problem, predictors are assumed to have a natural group structure and the goal is to select a small set of groups that best fits the response. The inco
Externí odkaz:
http://arxiv.org/abs/2404.13339
The Lasso regression is a popular regularization method for feature selection in statistics. Prior to computing the Lasso estimator in both linear and generalized linear models, it is common to conduct a preliminary rescaling of the feature matrix to
Externí odkaz:
http://arxiv.org/abs/2311.11236
We propose a continuous optimization algorithm for the Column Subset Selection Problem (CSSP) and Nystr\"om approximation. The CSSP and Nystr\"om method construct low-rank approximations of matrices based on a predetermined subset of columns. It is w
Externí odkaz:
http://arxiv.org/abs/2304.09678
We offer a method to estimate a covariance matrix in the special case that \textit{both} the covariance matrix and the precision matrix are sparse --- a constraint we call double sparsity. The estimation method is maximum likelihood, subject to the d
Externí odkaz:
http://arxiv.org/abs/2108.06638
In addition to recent developments in computing speed and memory, methodological advances have contributed to significant gains in the performance of stochastic simulation. In this paper, we focus on variance reduction for matrix computations via mat
Externí odkaz:
http://arxiv.org/abs/2106.14565
Autor:
Botev, Zdravko I., L'Ecuyer, Pierre
We propose and analyze a generalized splitting method to sample approximately from a distribution conditional on the occurrence of a rare event. This has important applications in a variety of contexts in operations research, engineering, and computa
Externí odkaz:
http://arxiv.org/abs/1909.03566
We propose a unified rare-event estimator for the performance evaluation of wireless communication systems. The estimator is derived from the well-known multilevel splitting algorithm. In its original form, the splitting algorithm cannot be applied t
Externí odkaz:
http://arxiv.org/abs/1908.10616
Publikováno v:
Studies in Applied Mathematics 145 (2020) 357-396
Kernel density estimation on a finite interval poses an outstanding challenge because of the well-recognized bias at the boundaries of the interval. Motivated by an application in cancer research, we consider a boundary constraint linking the values
Externí odkaz:
http://arxiv.org/abs/1809.07735
We consider a network whose links have random capacities and in which a certain target amount of flow must be carried from some source nodes to some destination nodes. Each destination node has a fixed demand that must be satisfied and each source no
Externí odkaz:
http://arxiv.org/abs/1805.03326