title: Method of median semi-variance for the analysis of left-censored data: comparison with other techniques using environmental data.
abstract: In environmental monitoring, variables with analytically non-detected values are commonly encountered. For the statistical evaluation of these data, most of the methods that produce a less biased performance require specific computer programs. In this paper, a statistical method based on the median semi-variance (SemiV) is proposed to estimate the position and spread statistics in a dataset with single left-censoring. The performances of the SemiV method and 12 other statistical methods are evaluated using real and complete datasets. The performances of all the methods are influenced by the percentage of censored data. In general, the simple substitution and deletion methods showed biased performance, with exceptions for L/2, Inter and L/√2 methods that can be used with caution under specific conditions. In general, the SemiV method and other parametric methods showed similar performances and were less biased than other methods. The SemiV method is a simple and accurate procedure that can be used in the analysis of datasets with less than 50% of left-censored data.
authors.affiliation: Department of Soils, Rio de Janeiro Federal Rural University, Seropédica, RJ, Brazil. Electronic address: zoffolihjo@yahoo.com.br.
authors.forename: Hugo José Oliveira
authors.initials: HJ
authors.lastname: Zoffoli
authors.forename: Carlos Alberto Alves
authors.initials: CA
authors.lastname: Varella
authors.forename: Nelson Moura Brasil
authors.initials: NM
authors.lastname: do Amaral-Sobrinho
authors.forename: Everaldo
authors.initials: E
authors.lastname: Zonta
authors.forename: Alfredo
authors.initials: A
authors.lastname: Tolón-Becerra
mesh_terms: D000704:Analysis of Variance; D003198:Computer Simulation; D004784:Environmental Monitoring / Q000379:methods*; D004787:Environmental Pollution / Q000706:statistics & numerical data*; D015233:Models, Statistical; D012107:Research Design; D012984:Software
keywords: Data imputation; Detection limit; Non-detected data; Trace elements