Estimators with Prescribed Precision in Stochastic Regression Models

Victor Konev

    Результат исследований: Материалы для журналаСтатья

    14 Цитирования (Scopus)

    Выдержка

    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.

    Язык оригиналаАнглийский
    Страницы (с-по)179-192
    Число страниц14
    ЖурналSequential Analysis
    Том14
    Номер выпуска3
    DOI
    СостояниеОпубликовано - 1 янв 1995

    Отпечаток

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

    ASJC Scopus subject areas

    • Modelling and Simulation
    • Statistics and Probability

    Цитировать

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

    В: Sequential Analysis, Том 14, № 3, 01.01.1995, стр. 179-192.

    Результат исследований: Материалы для журналаСтатья

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