On estimating population parameters in the presence of censored data: overview of available methods

Year 2014, Volume: 43 Issue: 2, 283 - 307, 01.04.2014

Abou El-makarim A. Aboueissa

Abstract

This paper examines recent results presented on estimating population
parameters in the presence of censored data with a single detection limit
(DL). The occurrence of censored data due to less than detectable measurements is a common problem with environmental data such as quality and quantity monitoring applications of water, soil, and air samples.
In this paper, we present an overview of possible statistical methods for
handling non-detectable values, including maximum likelihood, simple
substitution, corrected biased maximum likelihood, and EM algorithm
methods. Simple substitution methods (e.g. substituting 0, DL/2, or
DL for the non-detected values) are the most commonly used. It has
been shown via simulation that if population parameters are estimated
through simple substitution methods, this can cause significant bias
in estimated parameters. Maximum likelihood estimators may produce dependable estimates of population parameters even when 90% of
the data values are censored and can be performed using a computer
program written in the R Language. A new substitution method of
estimating population parameters from data contain values that are
below a detection limit is presented and evaluated. Worked examples
are given illustrating the use of these estimators utilizing computer
program. Copies of source codes are available upon request.

Keywords

detection limits, censored data, normal and lognormal distributions, likelihood function, maximum likelihood estimators

References

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