Improvements in Maximum Likelihood Estimators of Truncated Normal Samples with Prior Knowledge of O: A Simulation Based Study with Application to Historical Height Samples

27 Pages Posted: 4 Dec 2003

See all articles by B. A'Hearn

B. A'Hearn

Franklin & Marshall College - Department of Economics

John Komlos

Ludwig Maximilian University of Munich (LMU) - Faculty of Economics; CESifo (Center for Economic Studies and Ifo Institute)

Abstract

Researchers analyzing historical data on human stature have long sought an estimator that performs well in truncated-normal samples. This paper reviews that search, focusing on two currently widespread procedures: truncated least squares (TLS) and truncated maximum likelihood (TML). The first suffers from bias. The second suffers in practical application from excessive variability. A simple procedure is developed to convert TLS truncated means into estimates of the underlying population means, assuming the contemporary population standard deviation. This procedure is shown to be equivalent to restricted TML estimation. Simulation methods are used to establish the mean squared error performance characteristics of the restricted and unconstrained TML estimators in relation to several population and sample parameters. The results provide general insight into the bias-precision tradeoff in restricted estimation and a specific practical guide to optimal estimator choice for researchers in anthropometrics.

Keywords: Historical Height Samples, Simulation, Maqximum Likelihood Estimators, Truncated Samples

JEL Classification: I12, N00

Suggested Citation

A'Hearn, B. and Komlos, John, Improvements in Maximum Likelihood Estimators of Truncated Normal Samples with Prior Knowledge of O: A Simulation Based Study with Application to Historical Height Samples. Available at SSRN: https://ssrn.com/abstract=464060 or http://dx.doi.org/10.2139/ssrn.464060

B. A'Hearn

Franklin & Marshall College - Department of Economics ( email )

P. O. Box 3003
Lancaster, PA 17604-3003
United States
717-291-3926 (Phone)
717-291-4369 (Fax)

John Komlos (Contact Author)

Ludwig Maximilian University of Munich (LMU) - Faculty of Economics ( email )

Ludwigstrasse 28
Munich, D-80539
Germany

CESifo (Center for Economic Studies and Ifo Institute)

Poschinger Str. 5
Munich, DE-81679
Germany

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