Results 31 to 40 of about 379,537 (253)
Globally Sparse PLS Regression [PDF]
, 2013 Partial least squares (PLS) regression combines dimensionality reduction and prediction using a latent variable model. It provides better predictive ability than principle component analysis by taking into account both the independent and re- sponse variables in the dimension reduction procedure.
Liu, Tzu-Yu +4 more
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A Unified Framework for Sparse Relaxed Regularized Regression: SR3
IEEE Access, 2019 Regularized regression problems are ubiquitous in statistical modeling, signal processing, and machine learning. Sparse regression, in particular, has been instrumental in scientific model discovery, including compressed sensing applications, variable ...
Peng Zheng +4 more
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Sparse Online Variational Bayesian Regression
SIAM/ASA Journal on Uncertainty Quantification, 2022 This work considers variational Bayesian inference as an inexpensive and scalable alternative to a fully Bayesian approach in the context of sparsity-promoting priors. In particular, the priors considered arise from scale mixtures of Normal distributions with a generalized inverse Gaussian mixing distribution.
Kody J. H. Law, Vitaly Zankin
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Sparse relative risk regression models [PDF]
Biostatistics, 2018 SummaryClinical studies where patients are routinely screened for many genomic features are becoming more routine. In principle, this holds the promise of being able to find genomic signatures for a particular disease. In particular, cancer survival is thought to be closely linked to the genomic constitution of the tumor.
Wit, Ernst C +4 more
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Sequential Scaled Sparse Factor Regression [PDF]
Journal of Business & Economic Statistics, 2020 Large-scale association analysis between multivariate responses and predictors is of great practical importance, as exemplified by modern business applications including social media marketing and crisis management. Despite the rapid methodological advances, how to obtain scalable estimators with free tuning of the regularization parameters remains ...
Zheng, Zemin +3 more
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IEEE Access, 2019 Neuroimaging techniques have been used for automatic diagnosis and classification of Alzheimer's disease and mild cognitive impairment. How to select discriminant features from these data is the key that will affect the subsequent automatic diagnosis and
Lina Xu +5 more
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Sparse Regression by Projection and Sparse Discriminant Analysis. [PDF]
J Comput Graph Stat, 2015 Recent years have seen active developments of various penalized regression methods, such as LASSO and elastic net, to analyze high dimensional data. In these approaches, the direction and length of the regression coefficients are determined simultaneously.
Qi X, Luo R, Carroll RJ, Zhao H.
europepmc +5 more sources
Sparse Regression Codes for Multi-terminal Source and Channel Coding [PDF]
, 2012 We study a new class of codes for Gaussian multi-terminal source and channel coding. These codes are designed using the statistical framework of high-dimensional linear regression and are called Sparse Superposition or Sparse Regression codes.
Tatikonda, Sekhar, Venkataramanan, Ramji
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Lubricants, 2019 Data-driven system identification procedures have recently enabled the reconstruction of governing differential equations from vibration signal recordings.
Marco Didonna +5 more
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Sparse partial robust M regression [PDF]
Chemometrics and Intelligent Laboratory Systems, 2015 Sparse partial robust M regression is introduced as a new regression method. It is the first dimension reduction and regression algorithm that yields estimates with a partial least squares alike interpretability that are sparse and robust with respect to both vertical outliers and leverage points. A simulation study underpins these claims.
Hoffmann, Irene +3 more
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