point and interval evaluation of nanoparticles censored sample in the spray process

A good nano coating depends on the quality of the collision and spreading behavior of the nanoparticles. unfortunately, in many cases, nanoparticle spreading data has not been recorded. in this paper, we have extended the evaluation model to predict the unavailable or censored maximum spreading diameter of nanoparticle data. different point and interval methods have been considered for this problem. choosing bayesian evaluation, the markov chain monte carlo (mcmc) has been proposed as an efficient procedure for estimating the predictive inference for future observation. an important implication of the present study is that the censored maximum diameter data can be predicted well using the proposed methods. results showed the proposed point predictions are close to real data, the predictive intervals contain the real values, and it verifies the applicability of the prediction techniques for real problems.