On estimators for truncated height samples

Jan Jacobs*, Tomek Katzur, Vincent Tassenaar

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

6 Citations (Scopus)


Statistical inference from truncated height data is often based on distributional assumptions. In this paper we analyze a data set of over 23,000 conscript height observations, covering nearly all conscripts in Drenthe, a province of the Netherlands, over the period 1826-1860, The data do not satisfy the normality assumption. We demonstrate that the ML estimators of the mean proposed for normally distributed data do not yield satisfactory results. We propose a new estimator that exploits the relationship between the conditional mean of the observations above the minimum height requirement and the conditional mean and proportion of conscripts below the minimum height requirement. (C) 2007 Elsevier B.V. All rights reserved.

Original languageEnglish
Pages (from-to)43-56
Number of pages14
JournalEconomics & Human Biology
Issue number1
Publication statusPublished - Mar-2008


  • anthropometric history
  • physical stature
  • truncated distribution
  • maximum likelihood
  • normality
  • assumption
  • 19th century
  • drenthe
  • the Netherlands

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