# Asymptotically stable estimators of location and scale parameters II. Estimation of scale parameter

Mathematica Applicanda (1987)

- Volume: 16, Issue: 30
- ISSN: 1730-2668

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topTomasz Rychlik. "Asymptotically stable estimators of location and scale parameters II. Estimation of scale parameter." Mathematica Applicanda 16.30 (1987): null. <http://eudml.org/doc/293486>.

@article{TomaszRychlik1987,

abstract = {Asymptotic robustness of estimators of scale parameter with respect to scale invariant bias oscillation function is studied for two types of disturbances. In the case of £-contamination, the most robust sequence of equivariant estimators for model distribution with a positive support and the most robust sequence of equivariant symmetric estimators for symmetric model distribution are constructed. In the case of Kolmogorov-Levy neighbourhoods, the solution is derived without any assumptions about the model distribution. As examples, the most bias-robust estimators for uniform, Pareto, Weibull, Laplace, normal, Cauchy and double-exponential distributions are presented.},

author = {Tomasz Rychlik},

journal = {Mathematica Applicanda},

keywords = {Robustness and adaptive procedures; Point estimation},

language = {eng},

number = {30},

pages = {null},

title = {Asymptotically stable estimators of location and scale parameters II. Estimation of scale parameter},

url = {http://eudml.org/doc/293486},

volume = {16},

year = {1987},

}

TY - JOUR

AU - Tomasz Rychlik

TI - Asymptotically stable estimators of location and scale parameters II. Estimation of scale parameter

JO - Mathematica Applicanda

PY - 1987

VL - 16

IS - 30

SP - null

AB - Asymptotic robustness of estimators of scale parameter with respect to scale invariant bias oscillation function is studied for two types of disturbances. In the case of £-contamination, the most robust sequence of equivariant estimators for model distribution with a positive support and the most robust sequence of equivariant symmetric estimators for symmetric model distribution are constructed. In the case of Kolmogorov-Levy neighbourhoods, the solution is derived without any assumptions about the model distribution. As examples, the most bias-robust estimators for uniform, Pareto, Weibull, Laplace, normal, Cauchy and double-exponential distributions are presented.

LA - eng

KW - Robustness and adaptive procedures; Point estimation

UR - http://eudml.org/doc/293486

ER -

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