A modified robust confidence interval for the population mean of distribution based on deciles - Moustafa Omar Ahmed Abu-Shawiesh; Juthaphorn Sinsomboonthong; Bhuiyan Mohammad Golam Kibria (Statistics in Transition new series (SiTns), 2022)
The confidence interval is an important statistical estimator of population location and dispersion parameters. The paper considers a robust modified confidence interval, which is an adjustment of the Student's t confidence interval based on the decile mean and decile standard deviation for estimating the population mean of a skewed distribution. The efficiency of the proposed interval estimator is evaluated on the basis of an extensive Monte Carlo simulation study. The coverage ratio and average width of the proposed confidence interval are compared with certain existing and widely used confidence intervals. The simulation results show that, in general, the proposed interval estimator's performance is highly effective. For illustrative purposes, three real-life data sets are analyzed, which, to a certain extent, support the findings obtained from the simulation study. Thus, we recommend that practitioners use the robust modified confidence interval for estimating the population mean when the data are generated by a normal or skewed distribution.
Keywords: 519; robust confidence interval; decile mean; decile mean standard deviation; decile mean standard error; Monte Carlo simulation