Results 31 to 40 of about 24,904 (254)
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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Advanced Functional Materials, EarlyView.Nitride MXenes remain constrained by a persistent gap between computational prediction and experimental realization. This Review identifies the thermodynamic, kinetic, and chemical barriers limiting their synthesis, critically evaluates emerging fabrication routes, and proposes a multidimensional computational‐experimental framework to accelerate the ...
Naresh Varnakavi, Masoud Soroush
wiley +1 more source
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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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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Convex Regression with Interpretable Sharp Partitions. [PDF]
J Mach Learn Res, 2016 We consider the problem of predicting an outcome variable on the basis of a small number of covariates, using an interpretable yet non-additive model. We propose convex regression with interpretable sharp partitions (CRISP) for this task. CRISP partitions the covariate space into blocks in a data-adaptive way, and fits a mean model within each block ...
Petersen A, Simon N, Witten D.
europepmc +3 more sources
Advanced Materials, EarlyView.A solid‐state ionic actuator integrates sensing, actuation, and memory within one material. A brief UV pulse induces long‐lasting deformation via coupled network rearrangement, dehydration, and rehydration, while ionic redistribution enables self‐powered sensing, supporting multifunctional, energy‐efficient soft robotic functions without continuous ...
Yong Min Kim +5 more
wiley +1 more source
Advanced Materials, EarlyView.To address functionality limitations in plant‐based and industrially compostable polymer aerogels, this work introduces bio‐inspired aerogels with broadened functionality. Inspired by functional plant tissues, lightweight biopolymer aerogels based on exclusively stereocomplex crystals are fabricated, featuring hierarchically macro‐/meso‐porous ...
Anthony V. Tuccitto +10 more
wiley +1 more source
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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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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