Adaptive regression model for prediction of anthropometric data

Abstract

This paper presents and evaluates an adaptive linear regression model for the prediction of unknown anthropometric data based on a flexible set of known predictive data. The method is based on conditional regression and includes use of principal component analysis to reduce effects of multicollinearity between the predictive variables. Results from the study show that the proposed adaptive regression model produces more accurate predictions compared to a flat regression model based on stature and weight, and also compared to a hierarchical regression model, that uses geometric and statistical relationships between body measurements to create specific linear regression equations in a hierarchical structure. An additional evaluation shows that the accuracy of the adaptive regression model increases logarithmically with the sample size. Apart from the sample size, the accuracy of the regression model is affected by the number of, and on which measurements that are, variables in the predictive dataset.