Dependent versions of a central limit theorem for the squared length of a sample mean
AbstractPortnoy (1988) has proved a central limit theorem for the squared length of a sample mean by assuming that the underlying random vectors are independent and identically distributed and that their dimension increases with the sample size. Extensions of this result to martingale differences, useful in time series hypothesis testing, are derived and applied to a test of serial correlation.
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Bibliographic InfoArticle provided by Elsevier in its journal Statistics & Probability Letters.
Volume (Year): 22 (1995)
Issue (Month): 3 (February)
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description
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- Hansen, Bruce E., 1992. "Convergence to Stochastic Integrals for Dependent Heterogeneous Processes," Econometric Theory, Cambridge University Press, vol. 8(04), pages 489-500, December.
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