Grid multi-category response logistic models. [PDF]
BMC Med Inform Decis Mak, 2015 BackgroundMulti-category response models are very important complements to binary logistic models in medical decision-making. Decomposing model construction by aggregating computation developed at different sites is necessary when data cannot be moved ...
Wu Y +5 more
europepmc +5 more sources
Matching IRT Models to Patient-Reported Outcomes Constructs: The Graded Response and Log-Logistic Models for Scaling Depression. [PDF]
Psychometrika, 2021 Item response theory (IRT) model applications extend well beyond cognitive ability testing, and various patient-reported outcomes (PRO) measures are among the more prominent examples.
Reise SP +4 more
europepmc +2 more sources
Stochastic logistic models reproduce experimental time series of microbial communities. [PDF]
Elife, 2020 We analyze properties of experimental microbial time series, from plankton and the human microbiome, and investigate whether stochastic generalized Lotka-Volterra models could reproduce those properties.
Descheemaeker L, de Buyl S.
europepmc +2 more sources
Quantitative Models of Fungi Interaction--based on Logistic models [PDF]
E3S Web of Conferences, 2021 As the key medium for decomposing wood fibers, fungi play a vital role in promoting the carbon cycle. The purpose of this paper is to establish mathematic models describing the process of fungi decomposing litter and wood fiber. The paper comprehensively
Zhang Yunfei
doaj +1 more source
The asymptotic distribution of the MLE in high-dimensional logistic models: Arbitrary covariance [PDF]
Bernoulli, 2020 We study the distribution of the maximum likelihood estimate (MLE) in high-dimensional logistic models, extending the recent results from Sur (2019) to the case where the Gaussian covariates may have an arbitrary covariance structure.
Qian Zhao, P. Sur, E. Candès
semanticscholar +1 more source
The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression [PDF]
J. Am. Medical Informatics Assoc., 2022 Objective Methods to correct class imbalance (imbalance between the frequency of outcome events and nonevents) are receiving increasing interest for developing prediction models.
Ruben van den Goorbergh +3 more
semanticscholar +1 more source
Variable Selection for Spatial Logistic Autoregressive Models
Mathematics, 2022 When the spatial response variables are discrete, the spatial logistic autoregressive model adds an additional network structure to the ordinary logistic regression model to improve the classification accuracy. With the emergence of high-dimensional data
Jiaxuan Liang +4 more
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Group Logistic Regression Models with lp,q Regularization
Mathematics, 2022 In this paper, we proposed a logistic regression model with lp,q regularization that could give a group sparse solution. The model could be applied to variable-selection problems with sparse group structures. In the context of big data, the solutions for
Yanfang Zhang, Chuanhua Wei, Xiaolin Liu
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Machine Learning, 2003 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Landwehr, Niels, Hall, Mark, Frank, Eibe
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Machine Learning-based Classifiers for the Prediction of Low Birth Weight [PDF]
Healthcare Informatics Research, 2023 Objectives Low birth weight (LBW) is a global concern associated with fetal and neonatal mortality as well as adverse consequences such as intellectual disability, impaired cognitive development, and chronic diseases in adulthood.
Mahya Arayeshgari +4 more
doaj +1 more source