Invariance properties for the error function used for multilinear regression. [PDF]
PLoS ONE, 2018 The connections between the error function used in multilinear regression and the expected, or assumed, properties of the data are investigated. It is shown that two of the most basic properties often required in data analysis, scale and rotational ...
Mark H Holmes, Michael Caiola
doaj +5 more sources
Modular response analysis reformulated as a multilinear regression problem. [PDF]
Bioinformatics, 2023 AbstractMotivationModular response analysis (MRA) is a well-established method to infer biological networks from perturbation data. Classically, MRA requires the solution of a linear system and results are sensitive to noise in the data and perturbation intensities.
Borg JP, Colinge J, Ravel P.
europepmc +3 more sources
Multilinear tensor regression for longitudinal relational data [PDF]
The Annals of Applied Statistics, 2015 A fundamental aspect of relational data, such as from a social network, is the possibility of dependence among the relations. In particular, the relations between members of one pair of nodes may have an effect on the relations between members of another
Hoff, Peter D.
core +6 more sources
Multilinear Regression Analysis between Local Bioimpedance Spectroscopy and Fish Morphological Parameters [PDF]
Fishes, 2023 Repeated fish handling may cause stress, which biases experiments and so affects the results. In order to reduce this, the present study investigates the benefit of using bioimpedance analysis to estimate morphological parameters.
Vincent Kerzérho +10 more
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Modeling spatiotemporal patterns of microplastic pollution in the lupit river using multilinear regression [PDF]
Scientific ReportsDespite increasing awareness of microplastics as contaminants, their sources and abundance factors remain poorly understood. This study found widespread microplastic pollution in the Lupit River.
Katharina Raab +4 more
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Multilinear Kernel Regression and Imputation via Manifold Learning
IEEE Open Journal of Signal ProcessingThis paper introduces a novel kernel regression framework for data imputation, coined multilinear kernel regression and imputation via the manifold assumption (MultiL-KRIM).
Duc Thien Nguyen, Konstantinos Slavakis
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Estimating the valence and arousal of dyadic conversations using autonomic nervous system responses and regression algorithms [PDF]
Frontiers in NeuroergonomicsIntroductionAutonomic nervous system responses provide valuable information about interactions between pairs or groups of people but have primarily been studied using group-level statistical analysis, with a few studies attempting single-trial ...
Iman Chatterjee +5 more
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New reverse sum Revan indices for physicochemical and pharmacokinetic properties of anti-filovirus drugs [PDF]
Frontiers in ChemistryEbola and Marburg viruses, biosafety level 4 pathogens, cause severe hemorrhaging and organ failure with high mortality. Although some FDA-approved vaccines or therapeutics like Ervebo for Zaire Ebola virus exist, still there is a lack of effective ...
W. Tamilarasi, B. J. Balamurugan
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Multilinear Regression Model to Estimate Mud Weight [PDF]
Nigerian Journal of Environmental Sciences and Technology, 2021 Estimation of mud weight poses a serious challenge to mud industries. In this study, a model was developed to tackle the problem of estimation of mud weight using multilinear regression techniques. The model was developed using data obtained from production records.
J. O. Oloro, T. E. Akhihiero
openaire +1 more source
Horizontal axis wind turbine modelling and data analysis by multilinear regression [PDF]
Mechanical Sciences, 2020 The modelling of each horizontal axis wind turbine (HAWT) differs due to variation in operating conditions, dynamic parameters, and components. Thus, the choice of profiles also varies for specific applications.
P. Tittus, P. M. Diaz
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