Robust estimation of the parameters of \(g\)-\textit{and}-\(h\) distributions, with applications to outlier detection

DOI10.1016/j.csda.2014.01.003zbMath1506.62198OpenAlexW2062247477WikidataQ37653436 ScholiaQ37653436MaRDI QIDQ1623475

Boris Iglewicz, Inna Chervoneva, Yihuan Xu

Publication date: 23 November 2018

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

Full work available at URL: https://doi.org/10.1016/j.csda.2014.01.003

zbMATH Keywords

robustmaximum likelihoodleast squaresquantilesindirect inferenceoutlier detection\(g\)-and-\(h\) distribution

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Robustness and adaptive procedures (parametric inference) (62F35)

Related Items (3)

Dynamic quantile function models ⋮ Efficient maximum approximated likelihood inference for Tukey's \(g\)-and-\(h\) distribution ⋮ Parameter estimation of Tukey-type distributions: A comparative analysis

Uses Software

• Bioconductor
• gcrma
• limma

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