Results 31 to 40 of about 4,991,757 (343)
Logistic Regression in Rare Events Data
Political Analysis, 2001 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”).
Gary King, Langche Zeng
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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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Purposeful selection of variables in logistic regression
Source Code for Biology and Medicine, 2008 BackgroundThe main problem in many model-building situations is to choose from a large set of covariates those that should be included in the "best" model.
Z. Bursac +3 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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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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Conditional logistic regression [PDF]
American Journal of Orthodontics and Dentofacial Orthopedics, 2017 In this article, we will describe how to analyze binary data from matched studies in orthodontics. We have previously discussed matched analysis for paired binary data (McNemar test), but now we will focus on the use of regression methods to model our data.1 The idea is the same as with simple logistic regression models for binary data2,3; however, we ...
Despina, Koletsi, Nikolaos, Pandis
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Predicting Bankruptcy with Robust Logistic Regression
, 2021 Using financial ratio data from 2006 and 2007, this study uses a three-fold cross validation scheme to compare the classification and pre- diction of bankrupt firms by robust logistic regression with the Bianco and Yohai (BY) estimator versus maximum ...
R. Hauser, David E. Booth
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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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International Journal of Consumer Studies, 2020 This study aims to empirically measure the distinctive characteristics of customers who did and did not order food through Online Food Delivery services (OFDs) during the COVID‐19 outbreak in India. Data are collected from 462 OFDs customers.
Sangeeta Mehrolia +2 more
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Multicollinearity in Logistic Regression Model -Subject Review- [PDF]
المجلة العراقية للعلوم الاحصائية, 2020 : The logistic regression model is one of the modern statistical methods developed to predict the set of quantitative variables (nominal or monotonous), and it is considered as an alternative test for the simple and multiple linear regression ...
Najlaa Saad Ibrahim +2 more
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