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ReLogit: Rare Events Logistic Regression [PDF]
Journal of Statistical Software, 2003 We study rare events data, binary dependent variables with dozens to thousands of times fewer ones (events, such as wars, vetoes, cases of political activism, or epidemiological infections) than zeros ("nonevents").
Langche Zeng, Gary King, Michael Tomz
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Fast binary logistic regression [PDF]
PeerJ Computer ScienceThis study presents a novel numerical approach that improves the training efficiency of binary logistic regression, a popular statistical model in the machine learning community.
Nurdan Ayse Saran, Fatih Nar
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Evaluating logistic regression and geographically weighted logistic regression models for predicting orange-fleshed sweet potato adoption intention in Benin [PDF]
Scientific ReportsThe low adoption rate of biofortified crops, like orange-fleshed sweet potatoes (OFSP), by farmers remains a major food security concern. Accurate forecasting models for OFSP adoption intention are essential for breeding and introduction projects.
Idrissou Ahoudou +8 more
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Regression Analysis with Scikit-learn (part 2 - Logistic)
The Programming Historian, 2022 This lesson is the second in a two-part lesson focusing on regression analysis. It provides an overview of logistic regression, how to use Python (scikit-learn) to make a logistic regression model, and a discussion of interpreting the results of such ...
Matthew J. Lavin
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Logistic regression applied to natural hazards: rare event logistic regression with replications [PDF]
Natural Hazards and Earth System Sciences, 2012 Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead ...
M. Guns, V. Vanacker
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A three-parameter logistic regression model
Statistical Theory and Related Fields, 2021 Dose–response experiments and data analyses are often carried out according to an optimal design under a model assumption. A two-parameter logistic model is often used because of its nice mathematical properties and plausible stochastic response ...
Xiaoli Yu, Shaoting Li, Jiahua Chen
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Loan classification using logistic regression
Informatika, 2023 Objectives. The studied problem of loan classification is particularly important for financial institutions, which must efficiently allocate monetary assets between entities as part of the provision of financial services.
U. I. Behunkou, M. Y. Kovalyov
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Logistic regression: A simple primer
Cancer Research, Statistics, and Treatment, 2021 Logistic regression is used to obtain the odds ratio in the presence of more than one explanatory variable. This procedure is quite similar to multiple linear regression, with the only exception that the response variable is binomial.
Ankita Pal
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Predicting Heart Disease using Logistic Regression
Knowledge Engineering and Data Science, 2022 A common risk of death is caused by heart disease. It is critical in the field of medicine to be able to diagnose cardiac disease in order to adequately prevent and treat patients.
Mochammad Anshori, M. Syauqi Haris
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Fuzzy Semi-Parametric Logistic Quantile Regression Model
Wasit Journal for Pure Sciences, 2023 In this paper, the fuzzy semi-parametric logistic quantile regression model was studied in the absence of special conditions in the classical regression models.
Ahmed Razzaq, Ayad H. shemaila
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