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arXiv · 1706.01419

Asymptotically normal estimators for Zipf's law

Mikhail Chebunin·Artyom Kovalevskii
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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.

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Mikhail Chebunin, Artyom Kovalevskii. 2017-06-05. Asymptotically normal estimators for Zipf's law. https://arxiv.org/abs/1706.01419

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