Article
Licensed
Unlicensed
Requires Authentication
Recursive random variables with subgaussian distributions
-
Ralph Neininger
Published/Copyright:
September 25, 2009
Summary
We consider sequences of random variables with distributions that satisfy recurrences as they appear for quantities on random trees, random combinatorial structures and recursive algorithms. We study the tails of such random variables in cases where after normalization convergence to the normal distribution holds. General theorems implying subgaussian distributions are derived. Also cases are discussed with non-Gaussian tails. Applications to the probabilistic analysis of algorithms and data structures are given.
Keywords: tail bound; large deviation principle; recursion; analysis of algorithms; subgaussian distribution
:
Published Online: 2009-09-25
Published in Print: 2005-02-01
© R. Oldenbourg Verlag, München
You are currently not able to access this content.
You are currently not able to access this content.
Articles in the same Issue
- Absolutely continuous optimal martingale measures
- Optimal choice of kn-records in the extreme value index estimation
- On stationary multiplier methods for the rounding of probabilities and the limiting law of the Sainte-Laguë divergence
- Recursive random variables with subgaussian distributions
- Change in non-parametric regression with long memory errors
Keywords for this article
tail bound;
large deviation principle;
recursion;
analysis of algorithms;
subgaussian distribution
Articles in the same Issue
- Absolutely continuous optimal martingale measures
- Optimal choice of kn-records in the extreme value index estimation
- On stationary multiplier methods for the rounding of probabilities and the limiting law of the Sainte-Laguë divergence
- Recursive random variables with subgaussian distributions
- Change in non-parametric regression with long memory errors
