Abstract All researches, under classical statistics, are based on determinate, crisp data to estimate the mean of population when auxiliary information is available. Such estimates often biased. The goal find best for unknown value with minimum square error (MSE). neutrosophic generalization statistics tackles vague, indeterminate, uncertain information. Thus, first time overcome issues estimat...

This paper suggests a modified new ratio-product-exponential imputation procedure to deal with missing data in order estimate finite population mean simple random sample without replacement. The bias and squared error of our proposed estimator are obtained the first degree approximation. We derive conditions for parameters under which has smaller than mean, ratio, product estimators. carry out ...

We propose a novel family of estimators for the population mean under non-response and obtain MSE equation suggested estimator each situation in theory. These theoretical conditions are applied to three popular data sets literature we see that more efficient than traditional estimators, such as ratio, regression Case 1; whereas, 2, also Unal-Kadilar exponential same sets.