A data-based method for selecting tuning parameters in minimum distance estimators

DOI10.1016/j.csda.2004.03.006zbMath1429.62135OpenAlexW2004362032MaRDI QIDQ957136

B. E. Eshmatov

Publication date: 26 November 2008

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2004.03.006

zbMATH Keywords

robustnessHellinger distanceminimum distance estimationdensity power divergenceasymptotic mean squared error

Mathematics Subject Classification ID

Density estimation (62G07) Robustness and adaptive procedures (parametric inference) (62F35)

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Uses Software

• R
• KernSmooth

Cites Work

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• A data-based method for selecting tuning parameters in minimum distance estimators
• Almost fully efficient and robust simultaneous estimation of location and scale parameters: A minimum distance approach
• Minimum Hellinger distance estimates for parametric models
• Efficiency versus robustness: The case for minimum Hellinger distance and related methods
• Smoothing methods in statistics
• The Influence Curve and Its Role in Robust Estimation
• Robust and efficient estimation by minimising a density power divergence
• The generalized kullback-leibler divergence and robust inference
• Robust Statistics

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