Transformation and weighting in regression
Transformation and weighting in regression
Robust regression and outlier detection
Robust regression and outlier detection
Technometrics
Diagnostics in transformation and weighted regression
Technometrics
A note on Box-Cox transformation diagnostics
Technometrics
Maximum trimmed likelihood estimators: a unified approach, examples, and algorithms
Computational Statistics & Data Analysis
Computing least trimmed squares regression with the forward search
Statistics and Computing
Editorial: Nonparametric and Robust Methods
Computational Statistics & Data Analysis
Maximum trimmed likelihood estimator for multivariate mixed continuous and categorical data
Computational Statistics & Data Analysis
Robust Box-Cox transformations based on minimum residual autocorrelation
Computational Statistics & Data Analysis
Robust diagnostics for the heteroscedastic regression model
Computational Statistics & Data Analysis
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The problems of non-normality or functional relationships between variables may often be simplified by an appropriate transformation. However, the evidence for transformations may sometimes depend crucially on one or a few observations. Therefore, the purpose of the paper is to develop a method that will not be influenced by potential outliers during the process of data transformations. The concepts of the least trimmed squares estimator and the trimmed likelihood estimator are used to obtain the robust transformation parameters. Furthermore, the proposed procedure unifies robust statistics and a diagnostic approach to deal with the outlier problem in the regression transformation.