Adaptive regression model for prediction of anthropometric data
Artikel i vetenskaplig tidskrift, 2017
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.
regression
Anthropometry
digital human modelling
multivariate
conditional
capability
PCA
DHM.