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

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.

KEYWORDS:

• Kernel functions
• least square support vector machine
• count data
• three level
• random effect
• machine learning
• Brucellosis
• longitudinal analysis
• hierarchical
• nonlinear regression

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This study was a part of PhD thesis of the first author in Biostatistics and funded by the Vice-Chancellor for Research and Technology of Hamadan University of Medical Sciences [grant number 9609286041].