2021
DOI: 10.3934/puqr.2021009
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Optimal unbiased estimation for maximal distribution

Abstract: <p style='text-indent:20px;'>Unbiased estimation for parameters of maximal distribution is a fundamental problem in the statistical theory of sublinear expectations. In this paper, we proved that the maximum estimator is the largest unbiased estimator for the upper mean and the minimum estimator is the smallest unbiased estimator for the lower mean.</p>

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Cited by 13 publications
(5 citation statements)
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“…For the calculation of the ship's energy consumption, fuel consumption was calculated as a function of the engine power at a given speed, and for the assessment of emissions, the parameters of the ship's fuel consumption, engine power and operating time were used [15,38]. For this calculation, the maximal distribution method was used utilizing data achieved by conducting experiments on simulators and real ships [39]. The maximal distribution method could be applied in case at least 5 measurements were carried out.…”
Section: Steps Of Research Methodologymentioning
confidence: 99%
“…For the calculation of the ship's energy consumption, fuel consumption was calculated as a function of the engine power at a given speed, and for the assessment of emissions, the parameters of the ship's fuel consumption, engine power and operating time were used [15,38]. For this calculation, the maximal distribution method was used utilizing data achieved by conducting experiments on simulators and real ships [39]. The maximal distribution method could be applied in case at least 5 measurements were carried out.…”
Section: Steps Of Research Methodologymentioning
confidence: 99%
“…Proposition 2.1 ensures that as n → ∞ and k = o(n), the estimators by Jin and Peng (2016) are sufficiently concentrated inside [µ, µ].…”
Section: Statistical Inference For Uncertain Distributionsmentioning
confidence: 99%
“…The number of distributions in the family can be infinite while explicit formulations of the distributions are not to be expected. The nonlinear expectation theory has been applied successfully in data analysis to account for distribution uncertainty (Jin and Peng, 2021;Ji et al, 2023;Peng et al, 2023).…”
Section: Introductionmentioning
confidence: 99%