The asymptotics of the least trimmed absolute deviations (LTAD) estimator

DOI10.1016/0167-7152(94)90007-8zbMath0797.62029OpenAlexW2069029567MaRDI QIDQ1324563

Mara Tableman

Publication date: 27 October 1994

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0167-7152(94)90007-8

zbMATH Keywords

consistencyasymptotic normalitycomparison of asymptotic varianceshalf-samplesleast sum of absolute deviationsleast trimmed absolute deviations estimator of locationLTAD estimatorRousseeuw's least median of squares

Mathematics Subject Classification ID

Asymptotic properties of nonparametric inference (62G20) Nonparametric robustness (62G35) Nonparametric estimation (62G05)

Related Items (13)

Optimization techniques for robust multivariate location and scatter estimation ⋮ Robust estimation in structured linear regression ⋮ Estimating the variance of the LAD regression coefficients. ⋮ Optimization techniques for multivariate least trimmed absolute deviation estimation ⋮ The least trimmed quantile regression ⋮ Robust regression with high coverage. ⋮ Leveraged least trimmed absolute deviations ⋮ Unnamed Item ⋮ A central limit theorem for multivariate generalized trimmed \(k\)-means ⋮ Modified least trimmed quantile regression to overcome effects of leverage points ⋮ Asymptotics for trimmed \(k\)-means and associated tolerance zones. ⋮ The influence functions for the least trimmed squares and the least trimmed absolute deviations estimators ⋮ Unconventional features of positive-breakdown estimators

Cites Work

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• The finite-sample breakdown point of the Oja bivariate median and of the corresponding half-samples version
• Descriptive statistics for multivariate distributions
• Nonparametric interval and point prediction using data trimmed by a Grubbs-type outlier rule
• The influence functions for the least trimmed squares and the least trimmed absolute deviations estimators
• Approximation Theorems of Mathematical Statistics
• Nonparametric Statistical Data Modeling

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