Regression diagnostics for response transformations in a partial linear model

부분선형모형에서 반응변수변환을 위한 회귀진단

  • Received : 2012.11.07
  • Accepted : 2012.12.07
  • Published : 2013.01.31


In the transformation of response variable in partial linear models outliers can cause a bad effect on estimating the transformation parameter, just as in the linear models. To solve this problem the processes of estimating transformation parameter and detecting outliers are needed, but have difficulties to be performed due to the arbitrariness of the nonparametric function included in the partial linear model. In this study, through the estimation of nonparametric function and outlier detection methods such as a sequential test and a maximum trimmed likelihood estimation, processes for transforming response variable robust to outliers in partial linear models are suggested. The proposed methods are verified and compared their effectiveness by simulation study and examples.


Box-Cox transformation;C-step;maximum trimmed likelihood estimation;robustness


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Supported by : 건국대학교