Percentile Matching Estimation of Uncertainty Distribution

Sampath, S.; Anjana, K.

doi:10.1186/s40467-016-0049-9

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…The principle is based on equating two values of cumulative distribution function with corresponding percentiles and then simultaneously solving resulting equations for unknown parameters. Following Marks [ 22 ], Zaka and Akhtar [ 29 ] and Sampath and Anjana [ 31 ], we have chosen P 25 and P 75 to be relatively more accurate in comparison to other pairs of percentiles.…”

Section: Methodsmentioning

confidence: 99%

Efficient estimation of Pareto model: Some modified percentile estimators

et al. 2018

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The article proposes three modified percentile estimators for parameter estimation of the Pareto distribution. These modifications are based on median, geometric mean and expectation of empirical cumulative distribution function of first-order statistic. The proposed modified estimators are compared with traditional percentile estimators through a Monte Carlo simulation for different parameter combinations with varying sample sizes. Performance of different estimators is assessed in terms of total mean square error and total relative deviation. It is determined that modified percentile estimator based on expectation of empirical cumulative distribution function of first-order statistic provides efficient and precise parameter estimates compared to other estimators considered. The simulation results were further confirmed using two real life examples where maximum likelihood and moment estimators were also considered.

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Section: Methodsmentioning

confidence: 99%

Efficient estimation of Pareto model: Some modified percentile estimators

et al. 2018

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“…In view of the uneven dispersion of the data set, the percentile statistics method is used to calculate the data distribution [21]. Percentile statistics is to arrange a set of data from small to large and calculate the corresponding cumulative percentile.…”

Section: Statisticsmentioning

confidence: 99%

Design of Growth Trend Map of Children and Adolescents Based on Bone Age

Chen

Shi

Mao

et al. 2022

Computational Intelligence and Neuroscience

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Accurate height prediction has important reference significance for the development of children and adolescents and the selection of athletes. The current mainstream height prediction methods include the B-P (Bayley–Pinneau) method and the TW2 (Tanner–Whitehouse) method. A large number of documents show that the B-P method and the TW2 method have relatively large deviations in the lifelong height prediction results of Chinese children and adolescents. Based on the data collected by the Chinese Adolescent Students’ Physical Fitness and Growth and Development Health Project in Zhejiang’s primary and secondary schools, this paper proposes a graph of height growth trends based on bone age. The height map of age has more reference value. Aiming at the feasibility of the height data in the statistical results, the interpolation prediction method is used to verify the data, and the height growth trend graph is drawn through the method of fitting. Validation results with actual data show that the average error of the lifetime height prediction of the height growth trend map proposed in this paper is 2.1 cm, which is 1.4 cm lower than the 3.5 cm error predicted by the B-P method and 0.4 cm lower than the 2.5 cm error predicted by the TW2 method.

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Percentile Matching Estimation of Uncertainty Distribution

Cited by 2 publications

References 7 publications

Efficient estimation of Pareto model: Some modified percentile estimators

Efficient estimation of Pareto model: Some modified percentile estimators

Design of Growth Trend Map of Children and Adolescents Based on Bone Age

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