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Normal approximations by Stein's method


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  • Yosef Rinott
  • Vladimir Rotar


Stein's method for normal approximations is explained, with some examples and applications. In the study of the asymptotic distribution of the sum of dependent random variables, Stein's method may be a very useful tool. We have attempted to write an elementary introduction. For more advanced introductions to Stein's method, see Stein (1986), Barbour (1997) and Chen (1998).

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Bibliographic Info

Article provided by Springer in its journal Decisions in Economics and Finance.

Volume (Year): 23 (2000)
Issue (Month): 1 ()
Pages: 15-29

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Handle: RePEc:spr:decfin:v:23:y:2000:i:1:p:15-29

Note: Received: 6 December 1999
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Cited by:
  1. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2013. "Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors," CeMMAP working papers CWP76/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  2. Christophe Ley & Yvik Swan, 2011. "A unified approach to Stein characterizations," Working Papers ECARES 2013/88988, ULB -- Universite Libre de Bruxelles.
  3. Yun-Xia Li & Jian-Feng Wang, 2008. "An application of Stein’s method to limit theorems for pairwise negative quadrant dependent random variables," Metrika, Springer, vol. 67(1), pages 1-10, January.


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