응용통계연구 (The Korean Journal of Applied Statistics)

  • 제23권4호
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  • Pages.759-765
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  • 2010
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  • 1225-066X(pISSN)
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  • 2383-5818(eISSN)
한국통계학회 (The Korean Statistical Society)
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New Calibration Methods with Asymmetric Data

  • Kim, Sung-Su (Department of Statistics, Kyungpook National University)
  • 투고 : 20100500
  • 심사 : 20100600
  • 발행 : 2010.08.31
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초록

In this paper, two new inverse regression methods are introduced. One is a distance based method, and the other is a likelihood based method. While a model is fitted by minimizing the sum of squared prediction errors of y's and x's in the classical and inverse methods, respectively. In the new distance based method, we simultaneously minimize the sum of both squared prediction errors. In the likelihood based method, we propose an inverse regression with Arnold-Beaver Skew Normal(ABSN) error distribution. Using the cross validation method with an asymmetric real data set, two new and two existing methods are studied based on the relative prediction bias(RBP) criteria.

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참고문헌

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피인용 문헌

  1. Inverse circular–circular regression vol.119, 2013, https://doi.org/10.1016/j.jmva.2013.04.011
  2. Skew Normal Boxplot and Outliers vol.19, pp.4, 2012, https://doi.org/10.5351/CKSS.2012.19.4.591