On estimators for truncated height samples
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.
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- Jarque, Carlos M. & Bera, Anil K., 1980. "Efficient tests for normality, homoscedasticity and serial independence of regression residuals," Economics Letters, Elsevier, vol. 6(3), pages 255-259.
- John Komlos, "undated".
"Shrinking in a Growing Economy? The Mystery of Physical Stature during the Industrial Revolution,"
Articles by John Komlos
7, Department of Economics, University of Munich.
- Komlos, John, 1998. "Shrinking in a Growing Economy? The Mystery of Physical Stature during the Industrial Revolution," The Journal of Economic History, Cambridge University Press, vol. 58(03), pages 779-802, September.
- A'Hearn, Brian & Komlos, John, 2003. "Improvements in Maximum Likelihood Estimators of Truncated Normal Samples with Prior Knowledge of σ," Discussion Papers in Economics 51, University of Munich, Department of Economics.
- Richard H. Steckel, 1995. "Stature and the Standard of Living," Journal of Economic Literature, American Economic Association, vol. 33(4), pages 1903-1940, December.
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