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

Tomasz 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 -