Results 41 to 50 of about 1,312,626 (282)
Adaptive Lasso and group-Lasso for functional Poisson regression
J. Mach. Learn. Res., 2014 High dimensional Poisson regression has become a standard framework for the analysis of massive counts datasets. In this work we estimate the intensity function of the Poisson regression model by using a dictionary approach, which generalizes the classical basis approach, combined with a Lasso or a group-Lasso procedure.
Ivanoff, Stéphane +2 more
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Bayesian Regularisation in Structured Additive Regression Models for Survival Data [PDF]
, 2008 During recent years, penalized likelihood approaches have attracted a lot of interest both in the area of semiparametric regression and for the regularization of high-dimensional regression models.
Konrath, Susanne +2 more
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Smart building uncertainty analysis via adaptive Lasso
IET Cyber-Physical Systems, 2017 Uncertainty analysis plays a pivotal role in identifying the important parameters affecting building energy consumption and estimate their effects at the early design stages.
Bingqing Lin, Bei Yu
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Comparison of linear regression models Ordinary Lasso, Adaptive Group Lasso and Ordinary Least Squares models in selecting effective characteristics to predict the expected return [PDF]
Iranian Journal of Finance, 1999 In this study, for the selection of the characteristics of the company that provides the incremental information to investors and financial analysts, the linear models are adapted by the ordinary Lasso method (Tibshirani, 1996), Adaptive Group LASSO (Zu,
Raheleh ossadat Mortazavi +3 more
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Plot of a comparison among the lqsso, lasso, and adaptive lasso.
, 2023 Plot of the lqsso penalty in comparison with the lasso and adaptive lasso penalty according to the plot C in Zou [6]. (TIFF)
Alireza Daneshvar (14636459) +1 more
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A Robust Variable Selection Method for Sparse Online Regression via the Elastic Net Penalty
Mathematics, 2022 Variable selection has been a hot topic, with various popular methods including lasso, SCAD, and elastic net. These penalized regression algorithms remain sensitive to noisy data.
Wentao Wang +4 more
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A fast adaptive Lasso for the cox regression via safe screening rules
, 2021 Some interesting recent studies have shown that safe feature elimination screening algorithms are useful alternatives in solving large scale and/or ultra-high-dimensional Lasso-type problems.
Wang, H +7 more
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Sparse Bayesian Learning Using Adaptive LASSO Priors
, 2022 To improve the recovery accuracy of sparse signal, we study on sparse Bayesian learning (SBL) algorithm using adaptive least absolute shrinkage and selection operator (LASSO) priors.
Bai, Zong Long +2 more
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Dianzi Jishu YingyongAgainst the backdrop of large-scale integration of distributed energy resources, such as photovoltaic and wind power, into the power grid, traditional methods for calculating loop-closing currents face challenges of insufficient accuracy and difficulty ...
Mao Hongbin +4 more
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On Lasso and adaptive Lasso for non-random sample in credit scoring [PDF]
Statistical Modelling, 2022 Prediction models in credit scoring are often formulated using available data on accepted applicants at the loan application stage. The use of this data to estimate probability of default (PD) may lead to bias due to non-random selection from the population of applicants.
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