Asymptotically normal estimators for Zipf's law
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.
 Publication:

arXiv eprints
 Pub Date:
 June 2017
 arXiv:
 arXiv:1706.01419
 Bibcode:
 2017arXiv170601419C
 Keywords:

 Mathematics  Statistics Theory