A New Model Selection Test with Application to the Censored Data of Carbon Nanotubes Coating

Model selection of nano and micro droplet spreading can be widely used to predict and optimize of different coating processes such as ink jet printing, spray painting and plasma spraying. the idea of model selection is beginning with a set of data and rival models to choice the best one. the decision making on this set is an important question in statistical inference. some tests and criteria are designed to answer to this question that which of the rival models is the best one. the purpose of this article is to propose a new interval say tracking interval for comparing the two rival models and examine its suitability in the spread data of carbon nanotubes coating. the proposed interval can be used for nonnested or nested models and whether both, one or neither is misspecified. an important implication of the present study is that if the rival models are really close, then the proposed interval instead of the other tests can be determined the equivalent models under censored data.