Estimators with Prescribed Precision in Stochastic Regression Models

Victor Konev

    Research output: Contribution to journalArticle

    14 Citations (Scopus)

    Abstract

    This paper presents stopping rules and associated estimators, with prescribed mean squared errors, of the regression parameters in stochastic regression models. The construction makes fundamental use of the martingale structure of least squares estimates or their modifications. For one-dimensional regressors, the stopping rules simply stop as soon as the conditional variance of the underlying martingale exceeds some suitably chosen threshold. We show how this idea can be modified for the case of multidimensional stochastic regressors.

    Original languageEnglish
    Pages (from-to)179-192
    Number of pages14
    JournalSequential Analysis
    Volume14
    Issue number3
    DOIs
    Publication statusPublished - 1 Jan 1995

    Fingerprint

    Stopping Rule
    Martingale
    Stochastic Model
    Regression Model
    Estimator
    Conditional Variance
    Least Squares Estimate
    Mean Squared Error
    Exceed
    Regression

    Keywords

    • autoregression
    • fixed precision estimators
    • martingales
    • sequential estimation
    • stochastic regressors

    ASJC Scopus subject areas

    • Modelling and Simulation
    • Statistics and Probability

    Cite this

    Estimators with Prescribed Precision in Stochastic Regression Models. / Konev, Victor.

    In: Sequential Analysis, Vol. 14, No. 3, 01.01.1995, p. 179-192.

    Research output: Contribution to journalArticle

    Konev, Victor. / Estimators with Prescribed Precision in Stochastic Regression Models. In: Sequential Analysis. 1995 ; Vol. 14, No. 3. pp. 179-192.
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