Results 1 to 10 of about 2,723 (94)
Efficient Post-Shrinkage Estimation Strategies in High-Dimensional Cox’s Proportional Hazards Models [PDF]
EntropyRegularization methods such as LASSO, adaptive LASSO, Elastic-Net, and SCAD are widely employed for variable selection in statistical modeling. However, these methods primarily focus on variables with strong effects while often overlooking weaker signals,
Syed Ejaz Ahmed +2 more
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Chronic Obstructive Pulmonary Disease: Novel Genes Detection with Penalized Logistic Regression [PDF]
Cell Journal, 2023 Objective: This study aimed to introduce novel techniques for identifying the genes associated with developingchronic obstructive pulmonary disease (COPD) and to prioritize COPD candidate genes using regression methods.Materials and Methods: This is a ...
Kimiya Gohari +4 more
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Jurnal Iktiologi Indonesia, 2021 Fisheries management or conservation requires information on length-weight relationship (LWR) for the fishing regulation and biomass estimation. This study aims to assess LWR estimation using two methods, regular and Bayesian hierarchical approached for big-eye Scad (Selar crumenophthalmus).
JW Mosse, VPY Likumahuwa, BG Hutubessy
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Regularized group regression methods for genomic prediction: Bridge, MCP, SCAD, group bridge, group lasso, sparse group lasso, group MCP and group SCAD [PDF]
BMC Proceedings, 2014 Genomic prediction is now widely recognized as an efficient, cost-effective and theoretically well-founded method for estimating breeding values using molecular markers spread over the whole genome. The prediction problem entails estimating the effects of all genes or chromosomal segments simultaneously and aggregating them to yield the predicted total
Ogutu, Joseph O, Piepho, Hans-Peter
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Complexity, 2022 In order to reduce the dimensionality of parameter space and enhance out-of-sample forecasting performance, this research compares regularization techniques with Autometrics in time-series modeling.
Sara Muhammadullah +5 more
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Sparse Non-negative Matrix Factorization Algorithm Based on Proximal Alternating Linearized Minimization [PDF]
Jisuanji gongcheng, 2019 This paper combinessparsity constraint and Proximal Alternating Linearized Minimization(PALM),proposes a Sparse Non-negative Matrix Factorization(SNMF) algorithm,called SNMF_PALM.The non-convex Smoothly Clipped Absolute Deviation(SCAD) function is used ...
WANG Jing,YANG Dan
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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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Improving Network Slimming With Nonconvex Regularization
IEEE Access, 2021 Convolutional neural networks (CNNs) have developed to become powerful models for various computer vision tasks ranging from object detection to semantic segmentation. However, most of the state-of-the-art CNNs cannot be deployed directly on edge devices
Kevin Bui +4 more
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BMC Psychiatry, 2020 Background College students are at an increased risk of psychiatric distress. So, identifying its important correlates using more reliable statistical models, instead of inefficient traditional variable selection methods like stepwise regression, is of ...
Mahya Arayeshgari +4 more
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IEEE Access, 2020 This paper proposes a new smoothly clipped absolute deviation (SCAD) regularized recursive subspace model identification algorithm with square root(SR) extended instrumental variable (EIV) and locally optimal variable forgetting factor (LOFF).
Jian-Qiang Lin +2 more
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