Results 41 to 50 of about 27,189 (192)
Isotonic and Convex Regression: A Review of Theory, Algorithms, and Applications
MathematicsShape-restricted regression provides a flexible framework for estimating an unknown relationship between input variables and a response when little is known about the functional form, but qualitative structural information is available. In many practical
Eunji Lim
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Distributed Quantile Regression with Non-Convex Sparse Penalties [PDF]
, 2023 The surge in data generated by IoT sensors has increased the need for scalable and efficient data analysis methods, particularly for robust algorithms like quantile regression, which can be tailored to meet a variety of situations, including nonlinear ...
Kumar Dasanadoddi Venkategowda, Naveen, +7 more
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Journal of Statistical Software, 2014 Convex optimization now plays an essential role in many facets of statistics. We briefly survey some recent developments and describe some implementations of these methods in R .
Roger Koenker, Ivan Mizera
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On the implementation of LIR: the case of simple linear regression with interval data [PDF]
, 2012 This paper considers the problem of simple linear regression with interval-censored data. That is, n pairs of intervals are observed instead of the n pairs of precise values for the two variables (dependent and independent).
Cattaneo, Marco E.G.V. +2 more
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On convex regression estimators
, 2010 A new nonparametric estimator of a convex regression function in any dimension is proposed and its convergence properties are studied. We start by using any estimator of the regression function and we \emph{convexify} it by taking the convex envelope of a sample of the approximation obtained.
Aguilera, Néstor E. +2 more
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A parallel method for large scale convex regression problems [PDF]
53rd IEEE Conference on Decision and Control, 2014 Convex regression (CR) problem deals with fitting a convex function to a finite number of observations. It has many applications in various disciplines, such as statistics, economics, operations research, and electrical engineering. Computing the least squares (LS) estimator via solving a quadratic program (QP) is the most common technique to fit a ...
Necdet S. Aybat, Zi Wang 0007
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An Efficient Minimax Optimal Estimator For Multivariate Convex Regression
CoRR, 2022 This work studies the computational aspects of multivariate convex regression in dimensions $d \ge 5$. Our results include the \emph{first} estimators that are minimax optimal (up to logarithmic factors) with polynomial runtime in the sample size for both $L$-Lipschitz convex regression, and $Γ$-bounded convex regression under polytopal support.
Gil Kur, Eli Putterman
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Remote SensingThe non-redescending convex functions degrade the filtering robustness, whereas the redescending non-convex functions improve filtering robustness, but they tend to converge towards local minima.
Shoupeng Li, Weiwei Liu
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Advances in Difference Equations, 2021 The forward–backward algorithm is a splitting method for solving convex minimization problems of the sum of two objective functions. It has a great attention in optimization due to its broad application to many disciplines, such as image and signal ...
Suthep Suantai +3 more
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Smoothing ADMM for Sparse-Penalized Quantile Regression With Non-Convex Penalties
IEEE Open Journal of Signal ProcessingThis paper investigates quantile regression in the presence of non-convex and non-smooth sparse penalties, such as the minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD).
Reza Mirzaeifard +3 more
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