A tail area influence function and its application to testing

DOI10.1080/07474948508836070zbMath0592.62030OpenAlexW2038723397MaRDI QIDQ3721604

C. A. Field, Elvezio Ronchetti

Publication date: 1985

Published in: Sequential Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/07474948508836070

zbMATH Keywords

robustnessinfluence functionpowerleveltail area influence functionM- statisticsgross-error model

Mathematics Subject Classification ID

Parametric hypothesis testing (62F03) Robustness and adaptive procedures (parametric inference) (62F35)

Related Items (13)

Von Mises approximation of the critical value of a test ⋮ A von Mises approximation to the small sample distribution of the trimmed mean ⋮ Small sample asymptotics: A review with applications to robust statistics ⋮ Robust inference by influence functions ⋮ Another look at the tail area influence function ⋮ Special cases of second order Wiener germ approximations ⋮ A linear approximation to the power function of a test ⋮ Chi-square tests under models close to the normal distribution ⋮ Unnamed Item ⋮ The \(p\) value line: a way to choose the tuning constant in tests based on the Huber M-estimator ⋮ Saddlepoint approximations for the distribution of some robust estimators of the variogram ⋮ Approximations for \(F\) -tests which are ratios of sums of squares of independent variables with a model close to the normal ⋮ Sensitivity of the test error probabilities with respect to the level of contamination in general model of contaminacy

Cites Work

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• Small sample asymptotic expansions for multivariate M-estimates
• Estimates derived from robust tests
• Asymptotic lognormality of P-values
• A robust asymptotic testing model
• Saddlepoint approximations for estimating equations
• Saddle point approximation for the distribution of the sum of independent random variables
• A new infinitesimal approach to robust estimation
• Influence Functions for Testing
• Small-sample asymptotic distributions of M-estimators of location
• Robust Statistics

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