Composite scale modeling in the presence of censored data

A composite scale modeling approach can be used to combine several scales or variables into a single scale or variable. A typical application is to combine age and usage together to form a composite timescale model. The combined scale is expected to have better failure prediction capability than individual scales. Two typical models are the linear and multiplicative models. Their parameters are determined by minimizing the sample coefficient of variation of the composite scale. The minimum coefficient of variation is hard to apply in the presence of censored data. Another open issue is how to identify key variables when a number of variables are combined. This paper develops methods to handle these two issues. A numerical example is also included to illustrate the proposed methods.