Robust estimation for grouped data

Maria‐Pia Victoria‐Feser

doi:10.2307/2291478

Robust estimation for grouped data

1997-03-01

24

PRE

AI

M

Maria‐Pia Victoria‐Feser *

E

Elvezio Ronchetti

DOI：10.2307/2291478

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Abstract

En 中文

Here we investigate the robustness properties of the class of minimum power divergence estimators for grouped data. This class contains the classical maximum likelihood estimators for grouped data. We find that the bias of these estimators due to deviations from the assumed underlying model can be large. Therefore, we propose a more general class of estimators that allows us to construct robust procedures. By analogy with Hampel's theorem, we define optimal bounded influence function estimators, and by a simulation study, we show that under small model contaminations, these estimators are more stable than the classical estimators for grouped data. Finally, we apply our results to a particular real example.

Keywords:

diagnostics

grouping effects

influence function

local shift sensitivity

m estimators

minimum Hellinger distance estimators

minimum power divergence estimators

optimal b-robust estimators

Robust estimation for grouped data

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