Maximum likelihood estimation of the parameters of the inverse Gaussian distribution using maximum rank set sampling with unequal samples
Shuo Wang, Wangxue Chen, Meng Chen, Yawen Zhou
Abstract
Maximum ranked set sampling with unequal samples is a sampling procedure used to reduce the error of ranking of observations and increase the efficiency of statistical inference. It is used for maximum likelihood estimation of the location and shape parameters of the inverse Gaussian distribution. Its asymptotic efficiency is at least 1.4 times higher than those of estimators based on simple random sampling. It is useful in reliability studies and in Bayesian statistics involving the inverse Gaussian distribution.
Topics & Concepts
MathematicsStatisticsEstimatorSimple random sampleInverse Gaussian distributionSampling (signal processing)GaussianRanking (information retrieval)Rank (graph theory)Applied mathematicsDistribution (mathematics)PopulationComputer scienceCombinatoricsArtificial intelligenceFilter (signal processing)Mathematical analysisPhysicsDemographyComputer visionSociologyQuantum mechanicsStatistical Distribution Estimation and ApplicationsProbabilistic and Robust Engineering DesignReliability and Maintenance Optimization