Large Deviations of Estimators

A.D.M. Kester, W.C.M. Kallenberg

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    The performance of a sequence of estimators $\{T_n\}$ of $g(\theta)$ can be measured by its inaccuracy rate $-\lim \inf_{n\rightarrow\infty} n^{-1} \log \mathbb{P}_\theta(\|T_n - g(\theta)\| > \varepsilon)$. For fixed $\varepsilon > 0$ optimality of consistent estimators $\operatorname{wrt}$ the inaccuracy rate is investigated. It is shown that for exponential families in standard representation with a convex parameter space the maximum likelihood estimator is optimal. If the parameter space is not convex, which occurs for instance in curved exponential families, in general no optimal estimator exists. For the location problem the inaccuracy rate of $M$-estimators is established. If the underlying density is sufficiently smooth an optimal $M$-estimator is obtained within the class of translation equivariant estimators. Tail-behaviour of location estimators is studied. A connection is made between gross error and inaccuracy rate optimality.
    Original languageEnglish
    Pages (from-to)648-664
    JournalAnnals of statistics
    Issue number2
    Publication statusPublished - 1986


    • maximum likelihood estimator
    • translation equivariance
    • tail-behaviour
    • inaccuracy rate
    • exponential convexity
    • $M$-estimator
    • IR-70378
    • Large deviations


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