Stein’s Method: Some Perspectives with Applications

1998 ◽  
pp. 97-122 ◽  
Author(s):  
Louis H. Y. Chen
Keyword(s):  
Stein’S Method ◽  
Stein's Method

On the rate of Poisson process approximation to a Bernoulli process

1997 ◽  
Vol 34 (4) ◽  
pp. 898-907 ◽  
Author(s):  
Aihua Xia
Keyword(s):  
Poisson Process ◽  
Point Process ◽  
Discrete Time ◽  
Wasserstein Distance ◽  
Stein’S Method ◽  
Upper Bounds ◽  
Stein's Method ◽  
Bernoulli Process ◽  
Logarithmic Factor ◽  
Process Approximation

This note gives the rate for a Wasserstein distance between the distribution of a Bernoulli process on discrete time and that of a Poisson process, using Stein's method and Palm theory. The result here highlights the possibility that the logarithmic factor involved in the upper bounds established by Barbour and Brown (1992) and Barbour et al. (1995) may be superfluous in the true Wasserstein distance between the distributions of a point process and a Poisson process.

STEIN'S METHOD AND THE BERRY-ESSÉEN THEOREM

1984 ◽  
Vol 26 (1) ◽  
pp. 8-15 ◽  
Author(s):  
A. D. Barbour ◽  
Peter Hall
Keyword(s):  
Stein’S Method ◽  
Stein's Method

scholarly journals Stein’s method via induction

2020 ◽  
Vol 25 (0) ◽  
Author(s):  
Louis H.Y. Chen ◽  
Larry Goldstein ◽  
Adrian Röllin
Keyword(s):  
Stein’S Method ◽  
Stein's Method

scholarly journals Stein's Method and Descents after Riffle Shuffles

2005 ◽  
Vol 10 (0) ◽  
pp. 901-924
Author(s):  
Jason Fulman
Keyword(s):  
Stein’S Method ◽  
Stein's Method

scholarly journals CLT-related large deviation bounds based on Stein's method

2007 ◽  
Vol 39 (3) ◽  
pp. 731-752 ◽  
Author(s):  
Martin Raič
Keyword(s):  
Random Graphs ◽  
Finite Population ◽  
Large Deviation ◽  
Random Variables ◽  
Stein’S Method ◽  
Stein's Method ◽  
Population Statistics ◽  
Sums Of Random Variables ◽  
Dependence Structures

Large deviation estimates are derived for sums of random variables with certain dependence structures, including finite population statistics and random graphs. The argument is based on Stein's method, but with a novel modification of Stein's equation inspired by the Cramér transform.

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