Communications for Statistical Applications and Methods

  • 제19권6호
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  • Pages.793-798
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  • 2012
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  • 2287-7843(pISSN)
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  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
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Hidden Truncation Normal Regression

  • Kim, Sungsu (Applied Statistics Unit, Indian Statistical Institute)
  • 투고 : 2012.08.29
  • 심사 : 2012.10.12
  • 발행 : 2012.11.30
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초록

In this paper, we propose regression methods based on the likelihood function. We assume Arnold-Beaver Skew Normal(ABSN) errors in a simple linear regression model. It was shown that the novel method performs better with an asymmetric data set compared to the usual regression model with the Gaussian errors. The utility of a novel method is demonstrated through simulation and real data sets.

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참고문헌

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  8. Marazzi, A. and Yohai, V. (2002). Adaptive truncated maximum likelihood regression with asymmetric errors, Journal of Statistical Planning and Inference, 122, 271-291.
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