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Estimation of search tree size and approximate counting: A likelihood approach
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Florian Dennert
Veröffentlicht/Copyright:
25. September 2009
Abstract
We consider the problem of estimating the size of a random digital search tree on the basis of the maximal node depth observed along a specific path. We show that the maximum likelihood estimator exists and we investigate its properties. A similar problem arises in the context of approximate counting. In both cases a simple pure birth process plays a central role. We also construct confidence bounds.
Keywords: Birth process; confidence intervals; digital search tree algorithm; limit distribution; maximum likelihood estimation
Published Online: 2009-09-25
Published in Print: 2009-07
© by Oldenbourg Wissenschaftsverlag, München, Germany
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- Minimaxity of the Stein risk-minimization estimator for a normal mean matrix
- Estimation of search tree size and approximate counting: A likelihood approach
- Upper bounds for Bermudan options on Markovian data using nonparametric regression and a reduced number of nested Monte Carlo steps
- On a stochastic version of the trading rule “Buy and Hold”
- Characterization of optimal risk allocations for convex risk functionals
Schlagwörter für diesen Artikel
Birth process;
confidence intervals;
digital search tree algorithm;
limit distribution;
maximum likelihood estimation
Artikel in diesem Heft
- Minimaxity of the Stein risk-minimization estimator for a normal mean matrix
- Estimation of search tree size and approximate counting: A likelihood approach
- Upper bounds for Bermudan options on Markovian data using nonparametric regression and a reduced number of nested Monte Carlo steps
- On a stochastic version of the trading rule “Buy and Hold”
- Characterization of optimal risk allocations for convex risk functionals
