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Statistical estimation with bounded memory

    Research output: Contribution to journal › Article › peer-review

    Abstract

    We investigate bounded-memory estimators of statistical functionals. It is shown that, for nondegenerate functionals and stochastic processes, it is impossible to achieve consistent estimation with bounded memory. In the positive direction, we show that O(log(1/ε)) states suffice to achieve ε-consistent estimation for a natural class of functionals. A canonical optimal construction is conjectured for arbitrary statistical functionals.

    Original languageEnglish
    Pages (from-to)1155-1164
    Number of pages10
    JournalStatistics and Computing
    Volume22
    Issue number5
    DOIs
    StatePublished - 1 Sep 2012

    Keywords

    • Automaton
    • Bounded memory
    • DFA
    • Regular approximation
    • Statistical estimation

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • Statistics and Probability
    • Statistics, Probability and Uncertainty
    • Computational Theory and Mathematics

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