A mixed-effects least square support vector regression model for three-level count data. Issue 15 (13th October 2019)

• Record Type: Journal Article
• Authors: Moqaddasi Amiri, Mohammad; Tapak, Leili; Faradmal, Javad
• Is Part Of: Journal of statistical computation and simulation. Volume 89:Issue 15(2019)
• Page Start: 2801
• Page End: 2812
• Publication Date: 2019-10-13
• Subjects: Kernel functions -- least square support vector machine -- count data -- three level -- random effect -- machine learning -- Brucellosis -- longitudinal analysis -- hierarchical -- nonlinear regression; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2019.1636991
• Languages: English
• ISSNs: 0094-9655

Abstract

ABSTRACT: Hierarchical study design often occurs in many areas such as epidemiology, psychology, sociology, public health, engineering, and agriculture. This imposes correlation in data structure that needs to be account for in modelling process. In this study, a three-level mixed-effects least squares support vector regression (MLS-SVR) model is proposed to extend the standard least squares support vector regression (LS-SVR) model for handling cluster correlated data. The MLS-SVR model incorporates multiple random effects which allow handling unequal number of observations for each case at non-fixed time points (a very unbalanced situation) and correlation between subjects simultaneously. The methodology consists of a regression modelling step that is performed straightforwardly by solving a linear system. The proposed model is illustrated through numerical studies on simulated data sets and a real data example on human Brucellosis frequency. The generalization performance of the proposed MLS-SVR is evaluated by comparing to ordinary LS-SVR and some other parametric models.