This paper considers the application of method of percentile matching available in statistical theory of estimation for estimating the parameters involved in uncertainty distributions. An empirical study has been carried out to compare the performance of the proposed method with the method of moments and the method of least squares considered by Wang and Peng (J. Uncertainty Analys. Appl. 2, (2014)) and Liu (Uncertainty Theory: A Branch of Mathematics for Modeling Human Uncertainty, (2010)), respectively. The numerical study clearly establishes the superiority of the proposed method over the other two methods in estimating the parameters involved in linear uncertainty distribution when appropriate orders of percentiles are used in the estimation process.

Theproblemofestimatingparametersinvolvedinzigzaguncertaintydistributionisconsideredinthis article.Sensingthedifficultiesinvolvedinthedirectapplicationofstatisticalestimationtechniques foruncertaintydistribution,thepresentarticleconsiderstheapplicationofthemethodofpercentile matchingforestimatingtheunknownparametersofzigzaguncertaintydistribution.Thisarticleclearly establishesthefactthatthepercentilematchingmethodgivesbetterestimateswhencomparedtothe methodofmomentsifsamplepercentilesofappropriateordersareusedintheestimationprocess. Detailed numerical studies have been carried out using simulated datasets possessing different characteristicsforidentifyingoptimalordersofpercentileswhichgivebetterestimatesofparameters.