Results 11 to 20 of about 27,189 (192)
Estimating a Convex Function in Nonparametric Regression [PDF]
Scandinavian Journal of Statistics, 2006 Abstract. 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 integrated.
Dette, Holger, Birke, Melanie
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pyStoNED: A Python Package for Convex Regression and Frontier Estimation
Journal of Statistical SoftwareShape-constrained nonparametric regression is a growing area in econometrics, statistics, operations research, machine learning, and related fields.
Sheng Dai +3 more
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Max-Linear Regression by Convex Programming
IEEE Transactions on Information TheoryWe consider the multivariate max-linear regression problem where the model parameters $\boldsymbolβ_{1},\dotsc,\boldsymbolβ_{k}\in\mathbb{R}^{p}$ need to be estimated from $n$ independent samples of the (noisy) observations $y = \max_{1\leq j \leq k} \boldsymbolβ_{j}^{\mathsf{T}} \boldsymbol{x} + \mathrm{noise}$. The max-linear model vastly generalizes
Seonho Kim, Sohail Bahmani, Kiryung Lee
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Estimating a convex function in nonparametric regression [PDF]
, 2005 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 integrated.
Birke, Melanie
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Bayesian nonparametric multivariate convex regression
, 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. For example, in sequential decision processes the value of a state under optimal subsequent decisions may be known to be convex or concave. We propose a new Bayesian
Hannah, Lauren A., Dunson, David B.
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Automated resonance evaluation; Non-convex decomposition method for resonance regression and uncertainty quantification [PDF]
EPJ Web of Conferences, 2023 This work serves as a proof of concept for an automated tool to assist in the evaluation of experimental neutron cross section data in the resolved resonance range.
Walton Noah +3 more
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Smooth Strongly Convex Regression [PDF]
2020 28th European Signal Processing Conference (EUSIPCO), 2021 6 pages, 3 ...
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Efficiency of Indian General Insurance Companies: A Convex Nonparametric Least Squares Approach
Croatian Operational Research Review, 2022 In the current millennium, the Indian general insurance market has witnessed major structural changes because of the establishment of a market regulator and the initiation of entry deregulation.
Ram Pratap Sinha +2 more
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Freakonometrics, 2018 This morning during the lecture on nonlinear regression, I mentioned (very) briefly the case of convex regression. Since I forgot to mention the codes in R, I will publish them here.
Arthur Charpentier
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A dual active set algorithm for optimal sparse convex regression
Vestnik Samarskogo Gosudarstvennogo Tehničeskogo Universiteta. Seriâ: Fiziko-Matematičeskie Nauki, 2019 The shape-constrained problems in statistics have attracted much attention in recent decades. One of them is the task of finding the best fitting monotone regression. The problem of constructing monotone regression (also called isotonic regression) is to
Aleksandr A. Gudkov +3 more
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