The choice of sample size in estimating entropy according to a stratified sampling

  • María Angeles Gil
  • Covadonga Caso
Information Theory
Part of the Lecture Notes in Computer Science book series (LNCS, volume 313)


Havrda and Charvát (1967) and Daróczy (1970) introduced the nonadditive entropies of order α, each one of them can be regarded as a function defined on the class of distributions associated with a given population and quantifying, in a certain sense, the similarity between the uniform distribution and the considered one.

When the population is finite but too large to be censused, the entropy of order α = 2 may be unbiasedly estimated from a sample drawn at random from it. This estimation becomes specially useful when entropy is conceived as a measure of diversity within the population. In such a case, the populations to which this estimation is usually applied (e.g., anthropological, ecological, industrial, linguistic and sociological populations) often arise naturally stratified.

In the present paper, we are first going to discuss the problem of choosing a suitable sample size to estimate entropy on the basis of the information supplied by a pilot survey or a previous sample, drawn at random according to a stratified sampling with proportional allocation from the same or a similar population. We then establish an alternative and conservative criterion to choose the sample size, on the basis of the asymptotic distribution of the sample entropy.


Mutual Information Asymptotic Distribution Stratify Sampling Unbiased Estimator Stratify Random Sampling 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1988

Authors and Affiliations

  • María Angeles Gil
    • 1
  • Covadonga Caso
    • 1
  1. 1.Departamento de MatemáticasUniversidad de OviedoOviedoSpain

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