A Restricted Maximum Likelihood Estimator for Truncated Height Samples

Posted: 1 Apr 2004

Abstract

A restricted maximum likelihood (ML) estimator is presented and evaluated for use with truncated height samples. In the common situation of a small sample truncated at a point not far below the mean, the ordinary ML estimator suffers from high sampling variability. The restricted estimator imposes an a priori value on the standard deviation and freely estimates the mean, exploiting the known empirical stability of the former to obtain less variable estimates of the latter. Simulation results validate the conjecture that restricted ML behaves like restricted ordinary least squares (OLS), whose properties are well established on theoretical grounds. Both estimators display smaller sampling variability when constrained, whether the restrictions are correct or not. The bias induced by incorrect restrictions sets up a decision problem involving a bias-precision tradeoff, which can be evaluated using the mean squared error (MSE) criterion. Simulated MSEs suggest that restricted ML estimation offers important advantages when samples are small and truncation points are high, so long as the true standard deviation is within roughly 0.5 cm of the chosen value.

Keywords: Truncated-normal distributions, Maximum likelihood estimation, Mean squared error, Anthropometrics, Height estimation

JEL Classification: C4, I1, N0

Suggested Citation

A'Hearn, Brian, A Restricted Maximum Likelihood Estimator for Truncated Height Samples. Available at SSRN: https://ssrn.com/abstract=517162

Brian A'Hearn (Contact Author)

Pembroke College, Oxford ( email )

Department of Economics
Manor Road
Oxford, OX1 3UQ
United Kingdom

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