A mixed-effects least square support vector regression model for three-level count data

DOI10.1080/00949655.2019.1636991OpenAlexW2955981323WikidataQ127556841 ScholiaQ127556841MaRDI QIDQ5107494

Mohammad Moqaddasi Amiri, Leili Tapak, Javad Faradmal

Publication date: 27 April 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2019.1636991

zbMATH Keywords

count datanonlinear regressionmachine learningrandom effectbrucellosishierarchicalleast square support vector machinekernel functionslongitudinal analysisthree level

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• COUNT

Cites Work

• Unnamed Item
• A mixed effects least squares support vector machine model for classification of longitudinal data
• Weighted least squares support vector machines: robustness and sparse approximation
• Multiple kernel learning with random effects for predicting longitudinal outcomes and data integration
• Negative Binomial Regression
• Zero‐Inflated Poisson and Binomial Regression with Random Effects: A Case Study
• Zero-Inflated Negative Binomial Mixed Regression Modeling of Over-Dispersed Count Data with Extra Zeros
• Negative binomial loglinear mixed models
• Longitudinal Data Analysis
• A comparison of Bayesian and likelihood-based methods for fitting multilevel models

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