Results 71 to 80 of about 27,189 (192)

A Computational Framework for Multivariate Convex Regression and Its Variants [PDF]

, 2018
We study the nonparametric least squares estimator (LSE) of a multivariate convex regression function. The LSE, given as the solution to a quadratic program with O(n²) linear constraints (n being the sample size), is difficult to compute for large ...
Arkopal Choudhury, Bodhisattva Sen, Choudhury, Arkopal, Garud Iyengar, Mazumder, Rahul, Rahul Mazumder, Sen, Bodhisattva, Iyengar, Garud   +7 more
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An Unbiased Convex Estimator Depending on Prior Information for the Classical Linear Regression Model

Stats
We propose an unbiased restricted estimator that leverages prior information to enhance estimation efficiency for the linear regression model. The statistical properties of the proposed estimator are rigorously examined, highlighting its superiority over
Mustafa I. Alheety, HM Nayem, B. M. Golam Kibria   +2 more
doaj   +1 more source

Regression depth and support vector machine [PDF]

The regression depth method (RDM) proposed by Rousseeuw and Hubert [RH99] plays an important role in the area of robust regression for a continuous response variable.
Christmann, Andreas
core  

Face Image Recognition Method Using Non-Convex Mixed Norm Error Coding [PDF]

Jisuanji gongcheng
In response to the recognition challenges encountered by face images in complex environments with noise pollution, lighting variations, and occlusions, a face recognition method based on Non-convex Mixed-Norm error coding (NMN) is proposed.
GUO Junbo, MA Xiang
doaj   +1 more source

On robustness properties of convex risk minimization methods for pattern recognition [PDF]

The paper brings together methods from two disciplines: machine learning theory and robust statistics. Robustness properties of machine learning methods based on convex risk minimization are investigated for the problem of pattern recognition ...
Christmann, Andreas, Steinwart, Ingo
core  

Consistency of multidimensional convex regression

, 2020
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) multi-dimensional. Such regression problems arise in operations research, economics, and other
Peter W Glynn, Eunji Lim
core  

Consistency and robustness of kernel based regression [PDF]

We investigate properties of kernel based regression (KBR) methods which are inspired by the convex risk minimization method of support vector machines.
Christmann, Andreas, Steinwart, Ingo
core  

Robust regression with optimisation heuristics [PDF]

Linear regression is widely-used in finance. While the standard method to obtain parameter estimates, Least Squares, has very appealing theoretical and numerical properties, obtained estimates are often unstable in the presence of extreme observations ...
Enrico Schumann, Manfred Gilli
core  

Convex Loss Applied to Design in Regression Problems

Journal of the Royal Statistical Society Series B: Statistical Methodology, 1972
Summary A general linear regression function is to be observed at n points in order to estimate a known linear combination of the unknown parameters. The n points and the estimator are to be optimum in some sense and in this paper the main criterion for optimality involves uniformly minimizing certain convex loss functions.
openaire   +2 more sources

Shape constrained estimators in inverse regression models with convolution-type operator [PDF]

In this paper we are concerned with shape restricted estimation in inverse regression problems with convolution-type operator. We use increasing rearrangements to compute increasingand convex estimates from an (in principle arbitrary) unconstrained ...
Bissantz, Nicolai, Birke, Melanie
core