Results 231 to 240 of about 23,749 (256)

Consistency of Multidimensional Convex Regression

Operations Research, 2012
Convex regression is concerned with computing the best fit of a convex function to a data set of n observations in which the independent variable is (possibly) multidimensional. Such regression problems arise in operations research, economics, and other disciplines in which imposing a convexity constraint on the regression function is natural.
Eunji Lim
exaly   +2 more sources

On Convergence Rates of Convex Regression in Multiple Dimensions

INFORMS Journal on Computing, 2014
We consider a least squares estimator for estimating a convex function f*: [0, 1]d → ℝ with bounded subgradients. A rate at which the sum of squared differences between the estimator and the true function f* converges to zero is computed. This work sheds light on computing the convergence rate of the multidimensional convex regression estimator.
Eunji Lim
exaly   +2 more sources

On uniform consistent estimators for convex regression

Journal of Nonparametric Statistics, 2011
A new nonparametric estimator of a convex regression function in any dimension is proposed and its uniform convergence properties are studied.
Liliana Forzani, Pedro Morin
exaly   +2 more sources

Sparse Convex Regression

INFORMS Journal on Computing, 2021
We consider the problem of best [Formula: see text]-subset convex regression using [Formula: see text] observations in [Formula: see text] variables. For the case without sparsity, we develop a scalable algorithm for obtaining high quality solutions in practical times that compare favorably with other state of the art methods.
Dimitris Bertsimas, Nishanth Mundru
openaire   +1 more source

Regression Models for Convex ROC Curves

Biometrics, 2000
Summary. The performance of a diagnostic test is summarized by its receiver operating characteristic (ROC) curve. Under quite natural assumptions about the latent variable underlying the test, the ROC curve is convex. Empirical data on a test's performance often comes in the form of observed true positive and false positive relative frequencies under ...
openaire   +3 more sources

Convex Hull Ensemble Machine for Regression and Classification

Knowledge and Information Systems, 2004
We propose a new ensemble algorithm called Convex Hull Ensemble Machine (CHEM). CHEM in Hilbert space is first developed and modified for regression and classification problems. We prove that the ensemble model converges to the optimal model in Hilbert space under regularity conditions.
Yongdai Kim, Jinseog Kim
openaire   +1 more source

A convex version of multivariate adaptive regression splines

Computational Statistics & Data Analysis, 2015
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Diana L. Martinez, Dachuan T. Shih, Victoria C. P. Chen, Seoung Bum Kim   +3 more
openaire   +2 more sources

A study on comparison of convex and non-convex penalized regression methods

2023
In linear regression, penalized regression methods are used to obtain more accurate predictions depending on the structure of the data set. In addition, it is possible to determine the explanatory variables associated with the response variable by using penalized regression methods. In this study, the performances of ridge, LASSO, elastic net, adaptive
openaire   +1 more source

Convex Regression: Theory, Practice, and Applications

2016
This thesis explores theoretical, computational, and practical aspects of convex (shape-constrained) regression, providing new excess risk upper bounds, a comparison of convex regression techniques with theoretical guarantee, a novel heuristic training algorithm for max-affine representations, and applications in convex stochastic programming.
openaire   +1 more source