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- W205123838 abstract "The use of the Box-Cox family of transformations is a popular approach to make data behave according to a linear regression model. The regression coefficients, as well as the parameter λ defining the transformation, are generally estimated by maximum likelihood, assuming homoscedastic normal errors. These estimates are nonrobust; in addition, consistency to the true parameters holds only if the assumptions of normality and homoscedasticity are satisfied. We present here a new class of estimates, for the case of simple regression, which are robust and consistent even if the assumptions of normality and homoscedasticity do not hold.Mathematics Subject Classification (2000)Primary 62J05Secondary 62G35KeywordsBox-Cox transformationsrobust estimationheteroscedasticity" @default.
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- W205123838 date "2004-01-01" @default.
- W205123838 modified "2023-10-16" @default.
- W205123838 title "Robust Box-Cox Transformations for Simple Regression" @default.
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- W205123838 doi "https://doi.org/10.1007/978-3-0348-7958-3_16" @default.
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