Five-number summary

Abstract: Percentiles (or quantiles) are ubiquitous in descriptive as well as inferential analyses of data. Many applications in practice involve percentiles from the normal distribution. We consider confidence interval estimation of a normal distribution percentile and study several methods including the ones based on the maximum likelihood and the approximate normality of sample percentiles, that is, order statistics. The nonparametric confidence interval, based on the sign test, is included as a benchmark as it is a simple method and is valid for all continuous distributions. A Bayesian posterior predictive interval is also considered. The performance of the methods is examined in a simulation study via coverage and expected length. Summary and recommendations are given.

Abstract: When reporting the results of clinical studies, some researchers may choose the five-number summary (including the sample median, the first and third quartiles, and the minimum and maximum values) rather than the sample mean and standard deviation, particularly for skewed data. For these studies, when included in a meta-analysis, it is often desired to convert the five-number summary back to the sample mean and standard deviation. For this purpose, several methods have been proposed in the recent literature and they are increasingly used nowadays. In this paper, we propose to further advance the literature by developing a smoothly weighted estimator for the sample standard deviation that fully utilizes the sample size information. For ease of implementation, we also derive an approximation formula for the optimal weight, as well as a shortcut formula for the sample standard deviation. Numerical results show that our new estimator provides a more accurate estimate for normal data and also performs favorably for non-normal data. Together with the optimal sample mean estimator in Luo et al., our new methods have dramatically improved the existing methods for data transformation, and they are capable to serve as "rules of thumb" in meta-analysis for studies reported with the five-number summary. Finally for practical use, an Excel spreadsheet and an online calculator are also provided for implementing our optimal estimators.

Abstract: A mixture of Extreme Ranked Set Sampling (ERSS) and Median Ranked Set Sampling (MRSS) is introduced to obtain a more representative sample using three out of five number summary statistics [i.e., Minimum, Median and Maximum]. The proposed sampling scheme provides unbiased estimator of mean for symmetric population and gives moderate efficiency for both symmetric and asymmetric populations under perfect as well as imperfect rankings. Expressions for bias and asymptotic variance are presented. A simulation study is also conducted to observe the performance of the proposed estimator. Application of proposed sampling scheme is illustrated through a real life example.