Automatic robust Box-Cox and extended Yeo-Johnson transformations in regression
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- Marco Riani & Anthony C. Atkinson & Aldo Corbellini, 2023. "Automatic robust Box–Cox and extended Yeo–Johnson transformations in regression," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(1), pages 75-102, March.
References listed on IDEAS
- Riani, Marco & Atkinson, Anthony Curtis & Corbellini, Aldo & Farcomeni, Alessio & Laurini, Fabrizio, 2024.
"Information Criteria for Outlier Detection Avoiding Arbitrary Significance Levels,"
Econometrics and Statistics, Elsevier, vol. 29(C), pages 189-205.
- Riani, Marco & Atkinson, Anthony C. & Corbellini, Aldo & Farcomeni, Alessio & Laurini, Fabrizio, 2022. "Information criteria for outlier detection avoiding arbitrary significance levels," LSE Research Online Documents on Economics 113647, London School of Economics and Political Science, LSE Library.
- Tommaso Proietti & Marco Riani, 2009. "Transformations and seasonal adjustment," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(1), pages 47-69, January.
- Domenico Perrotta & Marco Riani & Francesca Torti, 2009. "New robust dynamic plots for regression mixture detection," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 3(3), pages 263-279, December.
- Marazzi, Alfio & Villar, Ana J. & Yohai, Victor J., 2009. "Robust Response Transformations Based on Optimal Prediction," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 360-370.
- Neykov, N. & Filzmoser, P. & Dimova, R. & Neytchev, P., 2007. "Robust fitting of mixtures using the trimmed likelihood estimator," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 299-308, September.
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More about this item
Keywords
Bayesian Information Criterion (BIC); constructed variable; extended coefficient of determination (R2); forward search; negative observations; simultaneous test; Department of Statistics;All these keywords.
JEL classification:
- C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
NEP fields
This paper has been announced in the following NEP Reports:- NEP-ECM-2022-07-25 (Econometrics)
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