Improving predictive accuracy of logistic regression model using ranked set samples

DOI10.1080/03610918.2014.955113zbMath1357.62259OpenAlexW2085452044MaRDI QIDQ2965556

Erniel B. Barrios, Kevin Carl P. Santos

Publication date: 3 March 2017

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2014.955113

zbMATH Keywords

ranked set samplinglogistic regression modelseparation of likelihoodrare characteristics

Mathematics Subject Classification ID

Generalized linear models (logistic models) (62J12) Sampling theory, sample surveys (62D05)

Related Items (1)

Further improving the performance of logistic regression analysis using double extreme ranking

Cites Work

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• Robust weighted kernel logistic regression in imbalanced and rare events data
• Improved attribute acceptance sampling plans based on maxima nomination sampling
• Control charts for attributes with maxima nominated samples
• On unbiased estimates of the population mean based on the sample stratified by means of ordering
• On the existence of maximum likelihood estimates in logistic regression models
• Efficient Regression Analysis with Ranked-Set Sampling
• An Introduction to Categorical Data Analysis
• Bias reduction of maximum likelihood estimates
• Parameters Estimation in a Simple Linear Regression Using Rank Set Sampling

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