Curved axes and trajectories for multidimensional scaling, with applications to sensory and consumer data

The analysis of sensory and consumer-derived data involves the use of many different statistical techniques. The vast majority of these are multivariate in for example, multidimensional scaling (MDS) and biplots. However, univariate techniques such as repeated measures analysis of variance and the Bradley-Terry model for paired comparison data are also common. This thesis introduces enhancements to MDS based on the use of curved axes and trajectories