Comparison of Record Ranked Set Sampling and Ordinary Records in Prediction of Future Record Statistics from an Exponential Distribution

In some situations, considering a suitable sampling scheme, to reduce the cost and increase efficiency is crucial. In this study, based on a record ranked set sampling scheme, the likelihood and Bayesian prediction of upper record values from a future sequence are discussed in the exponential model. To this end, under an upper record ranked set sample (RRSS) as an informative sample, the maximum likelihood as well as the Bayes point predictors for future upper record values under squared error (SE) and linear-exponential (LINEX) loss functions are obtained. Furthermore, based on a RRSS scheme, two Bayesian prediction intervals are presented. Prediction intervals are compared in terms of coverage probability and expected length. The results of the RRSS scheme are compared with the one based on ordinary records. Finally, a real data set concerning the daily heat degree is used to evaluate the theoretical results obtained. The results show that، in most of the situations, the RRSS scheme performs better.