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ЖУРНАЛЫ // Theory of Stochastic Processes // Архив

Theory Stoch. Process., 2016, том 21(37), выпуск 1, страницы 17–30 (Mi thsp117)

Asymptotic normality of linear regression parameter estimator in the case of random regressors

A. V. Ivanov, I. V. Orlovsky

National technical university of Ukraine ”KPI”, Department of mathematical analysis and probability theory, Peremogi avenue 37, Kiev, Ukraine

Аннотация: Sufficient conditions of asymptotic normality of the least squares estimator of linear regression model parameter in the case of discrete time and weak or long-range dependent random regressors and noise are obtained in the paper.

Ключевые слова: Asymptotic normality, least squares estimator, linear regression, random regressors, weak dependence, long-range dependence.

MSC: Primary 62J02; Secondary 62J99

Язык публикации: английский



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