Estimation of search tree size and approximate counting: A likelihood approach

Dennert Florian and Grübel Rudolf
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Dennert Florian: Leibniz Universität Hannover, Institut für Mathematische Stochastik, Hannover, Deutschland

Statistics & Risk Modeling, 2009, vol. 26, issue 4, 263-274

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 (search for similar items in EconPapers)
Date: 2009
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:strimo:v:26:y:2009:i:4:p:263-274:n:4

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DOI: 10.1524/stnd.2008.1016

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