Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/104123
Authors: 
A'Hearn, Brian
Komlos, John
Year of Publication: 
2003
Series/Report no.: 
Munich Discussion Paper 2003-8
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.
Subjects: 
truncated least squares
truncated maximum likelihood (TML)
simulation methods
bias-precision trade-off
anthropometrics
JEL: 
C1
C15
C24
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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