2013
DOI: 10.1080/00949655.2013.811669
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Outlier detection and robust estimation in linear regression models with fixed group effects

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Cited by 15 publications
(20 citation statements)
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“…Thus, the GPSC method is suitable for determining robust estimates for Kalimantan GRDP data. Perez et al (2013) compared the RDL1, M-S, and GPSC methods. The GPSC and MS methods by Moronna and Yohai (2000) gave almost similar estimation results to other methods, indicating their credibility.…”
Section: The Comparison Of Robust Estimates For Gross Regional Domest...mentioning
confidence: 99%
See 1 more Smart Citation
“…Thus, the GPSC method is suitable for determining robust estimates for Kalimantan GRDP data. Perez et al (2013) compared the RDL1, M-S, and GPSC methods. The GPSC and MS methods by Moronna and Yohai (2000) gave almost similar estimation results to other methods, indicating their credibility.…”
Section: The Comparison Of Robust Estimates For Gross Regional Domest...mentioning
confidence: 99%
“…This application of the GPSC method does not clearly state the type of data in each group. However, based on our study, the fixed group effects regression model used by Perez et al (2013) is more general than panel data because, in each group, there can be data from several objects in the group. If the data in each group is in the form of time series data, it means that the data is panel data.…”
Section: Introductionmentioning
confidence: 99%
“…Supposed that data are naturally distributed into groups and that a fixed group effect regression is postulated, Pérez et al (2014) compared three methods in terms of effectiveness in outlier detection and robustness via simulation studies in a wide array of contamination settings. The modified principal sensitivity component (PSC) procedure can detect true outliers and a small number of false outliers.…”
Section: Clustered and Dependent Observationsmentioning
confidence: 99%
“…in animal research. To build a regression model, researchers (Benyi, 1997;Atta and El khidir, 2004;Topal and Macit, 2004;Çankaya, 2009;Sarti et al, 2009;Çankaya et al, 2011) have frequently used least squares (LS) method, due to the simplicity of the idea of minimizing the sum of squared residuals and the interpretability of the final model parameter estimates (Pérez et al, 2013). Although LS method achieves optimum results when the underlying error distribution is Gaussian to estimate the weight of live animals, it brings some disadvantages.…”
Section: Introductionmentioning
confidence: 99%