A new bias-corrected estimator method in extreme value distributions with small sample size

DOI10.1080/00949655.2022.2085706OpenAlexW4282823965MaRDI QIDQ5879913

Sirao Wang, Kai-Tai Fang, Hua-Jun Ye

Publication date: 7 March 2023

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2022.2085706

zbMATH Keywords

maximum likelihood estimationMonte Carlo simulationextreme value distributionbias correctionsequential number-theoretic algorithm

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• LOWESS
• bootstrap
• nlpe

Cites Work

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• On the efficiency of certain quasi-random sequences of points in evaluating multi-dimensional integrals
• Bootstrap methods: another look at the jackknife
• Some applications of number-theoretic methods in statistics
• The Bias in Certain Estimates of the Parameters of the Extreme-Value Distribution
• Robust Locally Weighted Regression and Smoothing Scatterplots
• Modified maximum likelihood and modified moment estimators for the three-parameter weibull distribution
• Simplified Statistical Procedures for the Weibull or Extreme-Value Distribution
• Some Results on Point Estimation for the Two-Parameter Weibull or Extreme-Value Distribution
• Bias reduction of maximum likelihood estimates
• Bias-corrected maximum likelihood estimators of the parameters of the inverse Weibull distribution
• Inferences on the Parameters of the Weibull Distribution
• A Simplex Method for Function Minimization

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