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Binary logistic regression analysis
Psychiatry and Behavioral Sciences, 2015 In statistics, binary logistic regression analysis is a regression model where the dependent variable is a dichotomous categorical variable. The binary logistic model is used to estimate the probability of a binary response based on one or more ...
Selim Kılıc
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Bayesian logistic regression for presence-only data [PDF]
, 2015 Presence-only data are referred to situations in which a censoring mechanism acts on a binary response which can be partially observed only with respect to one outcome, usually denoting the \textit{presence} of an attribute of interest. A typical example
Antti Pettinen +3 more
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Privacy-preserving logistic regression training
BMC Medical Genomics, 2018 Background Logistic regression is a popular technique used in machine learning to construct classification models. Since the construction of such models is based on computing with large datasets, it is an appealing idea to outsource this computation to a
Charlotte Bonte, Frederik Vercauteren
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Bayesian Logistic Regression Model for Sub-Areas
Stats, 2023 Many population-based surveys have binary responses from a large number of individuals in each household within small areas. One example is the Nepal Living Standards Survey (NLSS II), in which health status binary data (good versus poor) for each ...
Lu Chen, Balgobin Nandram
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Barekeng, 2023 An understanding of factors that affect the recovery time from a disease is important for the community, medical staff, and also the government. This research analyzed factors that affect the recovery time of Covid-19 sufferers in West Sumatra.
Irvanal Haq +3 more
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TRANSFER LEARNING BASED ON LOGISTIC REGRESSION [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015 In this paper we address the problem of classification of remote sensing images in the framework of transfer learning with a focus on domain adaptation.
A. Paul, F. Rottensteiner, C. Heipke
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Privacy-preserving logistic regression with secret sharing
BMC Medical Informatics and Decision Making, 2022 Background Logistic regression (LR) is a widely used classification method for modeling binary outcomes in many medical data classification tasks. Researchers that collect and combine datasets from various data custodians and jurisdictions can greatly ...
Ali Reza Ghavamipour +2 more
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Maximal Uncorrelated Multinomial Logistic Regression
IEEE Access, 2019 Multinomial logistic regression (MLR) has been widely used in the field of face recognition, text classification, and so on. However, the standard multinomial logistic regression has not yet stressed the problem of data redundancy.
Dajiang Lei +4 more
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Kernel Logistic Regression-linear for Leukemia Classification Using High Dimensional Data [PDF]
, 2009 Kernel Logistic Regression (KLR) is one of the statistical models that has been proposed for classification in the machine learning and data mining communities, and also one of the effective methodologies in the kernel–machine techniques.
Embong, A. (A) +3 more
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Allergologia et Immunopathologia, 2011 In the health sciences it is quite common to carry out studies designed to determine the influence of one or more variables upon a given response variable. When this response variable is numerical, simple or multiple regression techniques are used, depending on the case.
S, Domínguez-Almendros +2 more
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