Results 11 to 20 of about 163,423 (274)
Non-crossing convex quantile regression
Economics Letters, 2023 Quantile crossing is a common phenomenon in shape constrained nonparametric quantile regression. A recent study by Wang et al. (2014) has proposed to address this problem by imposing non-crossing constraints to convex quantile regression. However, the non-crossing constraints may violate an intrinsic quantile property.
Sheng Dai, Timo Kuosmanen, Xun Zhou
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Convex support vector regression
European Journal of Operational ResearchNonparametric regression subject to convexity or concavity constraints is increasingly popular in economics, finance, operations research, machine learning, and statistics. However, the conventional convex regression based on the least squares loss function often suffers from overfitting and outliers.
Dai, Sheng +3 more
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Bayesian nonparametric multivariate convex regression [PDF]
, 2011 In many applications, such as economics, operations research and reinforcement learning, one often needs to estimate a multivariate regression function f subject to a convexity constraint.
Dunson, David B., Hannah, Lauren A.
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Nonconvex Sparse Logistic Regression With Weakly Convex Regularization [PDF]
IEEE Transactions on Signal Processing, 2018 In this work we propose to fit a sparse logistic regression model by a weakly convex regularized nonconvex optimization problem. The idea is based on the finding that a weakly convex function as an approximation of the $\ell_0$ pseudo norm is able to better induce sparsity than the commonly used $\ell_1$ norm.
Xinyue Shen, Yuantao Gu
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Indefinite Kernel Logistic Regression With Concave-Inexact-Convex Procedure [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2019 In kernel methods, the kernels are often required to be positive definite, which restricts the use of many indefinite kernels. To consider those non-positive definite kernels, in this paper, we aim to build an indefinite kernel learning framework for kernel logistic regression. The proposed indefinite kernel logistic regression (IKLR) model is analysed
Fanghui Liu +4 more
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Estimating a convex function in nonparametric regression [PDF]
Scandinavian Journal of Statistics, 2006 A new nonparametric estimate of a convex regression function is proposed and its stochastic properties are studied. The method starts with an unconstrained estimate of the derivative of the regression function, which is firstly isotonized and then ...
Birke, Melanie, Dette, Holger
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Convex Nonparanormal Regression [PDF]
IEEE Signal Processing Letters, 2021 Quantifying uncertainty in predictions or, more generally, estimating the posterior conditional distribution, is a core challenge in machine learning and statistics. We introduce Convex Nonparanormal Regression (CNR), a conditional nonparanormal approach for coping with this task.
Yonatan Woodbridge +2 more
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On Univariate Convex Regression [PDF]
Sankhya A, 2017 We find the local rate of convergence of the least squares estimator (LSE) of a one dimensional convex regression function when (a) a certain number of derivatives vanish at the point of interest, and (b) the true regression function is locally affine. In each case we derive the limiting distribution of the LSE and its derivative.
Ghosal, Promit, Sen, Bodhisattva
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Smooth Strongly Convex Regression [PDF]
2020 28th European Signal Processing Conference (EUSIPCO), 2021 6 pages, 3 ...
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Robust Variable Selection for Single-Index Varying-Coefficient Model with Missing Data in Covariates
Mathematics, 2022 As applied sciences grow by leaps and bounds, semiparametric regression analyses have broad applications in various fields, such as engineering, finance, medicine, and public health.
Yunquan Song, Yaqi Liu, Hang Su
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