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Regularity Properties for Sparse Regression. [PDF]
Commun Math Stat, 2016 Statistical and machine learning theory has developed several conditions ensuring that popular estimators such as the Lasso or the Dantzig selector perform well in high-dimensional sparse regression, including the restricted eigenvalue, compatibility ...
Dobriban E, Fan J.
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Correction: Multivariate functional group sparse regression: Functional predictor selection. [PDF]
PLoS ONE, 2023 [This corrects the article DOI: 10.1371/journal.pone.0265940.].
Ali Mahzarnia, Jun Song
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Multivariate functional group sparse regression: Functional predictor selection. [PDF]
PLoS ONE, 2022 In this paper, we propose methods for functional predictor selection and the estimation of smooth functional coefficients simultaneously in a scalar-on-function regression problem under a high-dimensional multivariate functional data setting.
Ali Mahzarnia, Jun Song
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Robust and Sparse Regression via γ-Divergence
Entropy, 2017 In high-dimensional data, many sparse regression methods have been proposed. However, they may not be robust against outliers. Recently, the use of density power weight has been studied for robust parameter estimation, and the corresponding divergences ...
Takayuki Kawashima, Hironori Fujisawa
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Discovery of Intermediary Genes between Pathways Using Sparse Regression. [PDF]
PLoS ONE, 2015 The use of pathways and gene interaction networks for the analysis of differential expression experiments has allowed us to highlight the differences in gene expression profiles between samples in a systems biology perspective.
Kuo-ching Liang +2 more
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Robust Joint Sparse Uncorrelated Regression [PDF]
Jisuanji kexue, 2022 Common unsupervised feature selection methods only consider the selection of discriminative features,while ignoring the redundancy of features and failing to consider the problem of small classes,which affect the classification performance.Based on this ...
LI Zong-ran, CHEN XIU-Hong, LU Yun, SHAO Zheng-yi
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Learning Interactions in Reaction Diffusion Equations by Neural Networks
Entropy, 2023 Partial differential equations are common models in biology for predicting and explaining complex behaviors. Nevertheless, deriving the equations and estimating the corresponding parameters remains challenging from data.
Sichen Chen +3 more
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Stochastic Parameterization Using Compressed Sensing: Application to the Lorenz-96 Atmospheric Model
Tellus: Series A, Dynamic Meteorology and Oceanography, 2022 Growing set of optimization and regression techniques, based upon sparse representations of signals, to build models from data sets has received widespread attention recently with the advent of compressed sensing.
A. Mukherjee +3 more
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Reliable Genetic Correlation Estimation via Multiple Sample Splitting and Smoothing
Mathematics, 2023 In this paper, we aim to investigate the problem of estimating the genetic correlation between two traits. Instead of making assumptions about the distribution of effect sizes of the genetic factors, we propose the use of a high-dimensional linear model ...
The Tien Mai
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Soft-Computing-Based Estimation of a Static Load for an Overhead Crane
Sensors, 2023 Payload weight detection plays an important role in condition monitoring and automation of cranes. Crane cells and scales are commonly used in industrial practice; however, when their installation to the hoisting equipment is not possible or costly, an ...
Tom Kusznir, Jaroslaw Smoczek
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