Robust Analysis of Covariance
Copyright policy )Robust Analysis of Covariance
The simple analysis of covariance situation with two groups and one concomitant variable is considered. The parameters of this model with outliers present are estimated by the methods of at least squares and M-estimation. By use of simulation, several forms of M-estimators are compared with the least squares method. In terms of their efficiencies the tests on the equality of slopes and of adjusted means in the presence of outliers are examined under the null hypothesis by studying the behavior of t-like statistics based on the least squares and M-estimates. An example illustrates the techniques discussed.
test for adjusted means, Statistics as Topic, Analysis of variance and covariance (ANOVA), tests on equality of slopes, general linear model, M-estimators, Models, Biological, Humans, presence of outliers, Robustness and adaptive procedures (parametric inference), Monte Carlo simulation, Probability, Analysis of Variance, Clinical Trials as Topic, efficiencies, test for parallelism, robust analysis of covariance, iterated reweighted least squares, Bacterial Infections, t-like statistics, F-like test statistics
test for adjusted means, Statistics as Topic, Analysis of variance and covariance (ANOVA), tests on equality of slopes, general linear model, M-estimators, Models, Biological, Humans, presence of outliers, Robustness and adaptive procedures (parametric inference), Monte Carlo simulation, Probability, Analysis of Variance, Clinical Trials as Topic, efficiencies, test for parallelism, robust analysis of covariance, iterated reweighted least squares, Bacterial Infections, t-like statistics, F-like test statistics
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