Asymptotic Theory of Taguchis Natural Estimators of the Signal to Noise Ratio for Dynamic Robust Parameter Design

Koji Tsukuda, Yasushi Nagata

    Research output: Contribution to journalArticle

    1 Citation (Scopus)

    Abstract

    This article discusses the asymptotic theory of Taguchis natural estimators of the signal to noise ratio (SNR) for dynamic robust parameter design. Three asymptotic properties are shown. First, two natural estimators of the population SNR are asymptotically equivalent. Second, both of these estimators are consistent. Finally, both of these estimators are asymptotically normally distributed.

    Original languageEnglish
    Pages (from-to)4734-4741
    Number of pages8
    JournalCommunications in Statistics - Theory and Methods
    Volume44
    Issue number22
    DOIs
    Publication statusPublished - 2015 Nov 17

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    Robust Parameter Design
    Asymptotic Theory
    Estimator
    Asymptotically equivalent
    Asymptotic Properties

    Keywords

    • Asymptotic normality
    • Consistency
    • Robust parameter design
    • Signal to noise ratio

    ASJC Scopus subject areas

    • Statistics and Probability

    Cite this

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    abstract = "This article discusses the asymptotic theory of Taguchis natural estimators of the signal to noise ratio (SNR) for dynamic robust parameter design. Three asymptotic properties are shown. First, two natural estimators of the population SNR are asymptotically equivalent. Second, both of these estimators are consistent. Finally, both of these estimators are asymptotically normally distributed.",
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    N2 - This article discusses the asymptotic theory of Taguchis natural estimators of the signal to noise ratio (SNR) for dynamic robust parameter design. Three asymptotic properties are shown. First, two natural estimators of the population SNR are asymptotically equivalent. Second, both of these estimators are consistent. Finally, both of these estimators are asymptotically normally distributed.

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    KW - Consistency

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