Subsample Goal Model for Multihalver on Outliers

Problem statement: In this study, a delete-half jackknife problem reformulated as a subsample multihalver was presented. Approach: In this respect, exploiting outlier nomination and estimation, since considering all possible half-sample is unpractical and unfeasible were considered. Results: We derived subsample algorithm which is unbiased multihalver and the performance of the model in formulating the subsample multihalver was shown. Conclusion: The result of subsample multihalver method of nomination and estimation is better way of resolving large population.