Asymptotically normal estimators for Zipf's law
Statistics Theory
2017-06-15 v2 Statistics Theory
Abstract
Zipf's law states that sequential frequencies of words in a text correspond to a power function. Its probabilistic model is an infinite urn scheme with asymptotically power distribution. The exponent of this distribution must be estimated. We use the number of different words in a text and similar statistics to construct asymptotically normal estimators of the exponent.
Cite
@article{arxiv.1706.01419,
title = {Asymptotically normal estimators for Zipf's law},
author = {Mikhail Chebunin and Artyom Kovalevskii},
journal= {arXiv preprint arXiv:1706.01419},
year = {2017}
}