Abstract
Abstract Nowadays in the competitive marketplace, manufacturers need to classify products in a short time according to market demand. Hence, it is a challenge for a manufacturer to implement a classification test that can distinguish the different levels of products quickly and efficiently. For highly reliable products, if quality characteristics do exist whose degradation over time can be related with the lifetime of the product, the degradation model can then be constructed based on the degradation data. In this study, we propose a non-linear degradation model that simultaneously considers unit-to-unit variation with time-dependent error structure and measurement error. Then, by adopting the concept of linear discriminant analysis, we also propose a three-step classification policy to determine optimal vector of coefficients, optimal cut-off point and optimal testing time subject to cost. In addition, we also use an analytic approach to compare the efficiency of our proposed procedure with two methods that is previously reported by Tseng & Tang (2001) and Tseng & Peng (2004). Finally, we use LED data to illustrate the proposed classification procedure. Key words: classification procedure, mixture Gaussian process, random effect, Wiener process, measurement error, linear discriminant analysis.