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PLS regression on a stochastic process [PDF]
Computational Statistics and Data Analysis, 2005 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Preda, Cristian, Saporta, Gilbert
exaly +4 more sources
Clusterwise PLS regression on a stochastic process [PDF]
Computational Statistics and Data Analysis, 2005 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Preda, Cristian, Saporta, Gilbert
exaly +3 more sources
Inferring structure and parameters of stochastic reaction networks with logistic regression. [PDF]
PLoS ONEIdentifying network structure and estimating reaction parameters remain central challenges in modeling chemical reaction networks. In this work, we develop likelihood-based methods that use multinomial logistic regression to infer both stoichiometries ...
Boseung Choi +2 more
doaj +2 more sources
Nonlinear stochastic modelling with Langevin regression [PDF]
Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2021 Many physical systems characterized by nonlinear multiscale interactions can be modelled by treating unresolved degrees of freedom as random fluctuations. However, even when the microscopic governing equations and qualitative macroscopic behaviour are known, it is often difficult to derive a stochastic model that is consistent with observations.
Callaham, J. +3 more
openaire +7 more sources
Regression and progression in stochastic domains [PDF]
Artificial Intelligence, 2020 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Belle, Vaishak; id_orcid 0000-0001-5573-8465 +1 more
openaire +2 more sources
Evaluation of Mutual Information and Feature Selection for SARS-CoV-2 Respiratory Infection
Bioengineering, 2023 This study aims to develop a predictive model for SARS-CoV-2 using machine-learning techniques and to explore various feature selection methods to enhance the accuracy of predictions. A precise forecast of the SARS-CoV-2 respiratory infections spread can
Sekar Kidambi Raju +6 more
doaj +1 more source
Granular Elastic Network Regression with Stochastic Gradient Descent
Mathematics, 2022 Linear regression is the use of linear functions to model the relationship between a dependent variable and one or more independent variables. Linear regression models have been widely used in various fields such as finance, industry, and medicine.
Linjie He +3 more
doaj +1 more source
Estimation of Stationary Stochastic Coefficient Regression Models [PDF]
The Egyptian Statistical Journal, 1972 In this paper an attempt is made to estimate a regression equation using a time series of cross-sections. It is assumed that the stochastic coefficient regression vector is distributed across individuals with the same mean and the same variance ...
Farid E. Abdel-Badie +1 more
doaj +1 more source
Periodic Fluctuations in the Incidence of Gastrointestinal Cancer
Frontiers in Oncology, 2021 PurposeNative stem cells can be periodically replaced during short and long epigenetic intervals. Cancer-prone new stem cells might bring about periodic (non-stochastic) carcinogenic events rather than stochastic events.
Mun-Gan Rhyu +5 more
doaj +1 more source
A New Lifetime Model, Stochastic Orders and Kidney Infection Regression Model [PDF]
Journal of Sciences, Islamic Republic of Iran, 2021 We introduce a method to generate a new class of lifetime models based on the bounded distributions such that the defined models are exclusively a special case of the new class.
Araf Khanjari Idenak +2 more
doaj +1 more source