응용통계연구 (The Korean Journal of Applied Statistics)

  • 제24권5호
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  • Pages.759-768
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  • 2011
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  • 1225-066X(pISSN)
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  • 2383-5818(eISSN)
한국통계학회 (The Korean Statistical Society)
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Tree-Structured Nonlinear Regression

  • 투고 : 20110600
  • 심사 : 20110800
  • 발행 : 2011.10.31
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초록

Tree algorithms have been widely developed for regression problems. One of the good features of a regression tree is the flexibility of fitting because it can correctly capture the nonlinearity of data well. Especially, data with sudden structural breaks such as the price of oil and exchange rates could be fitted well with a simple mixture of a few piecewise linear regression models. Now that split points are determined by chi-squared statistics related with residuals from fitting piecewise linear models and the split variable is chosen by an objective criterion, we can get a quite reasonable fitting result which goes in line with the visual interpretation of data. The piecewise linear regression by a regression tree can be used as a good fitting method, and can be applied to a dataset with much fluctuation.

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참고문헌

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피인용 문헌

  1. Panel data analysis with regression trees vol.25, pp.6, 2014, https://doi.org/10.7465/jkdi.2014.25.6.1253
  2. Variable selection with quantile regression tree vol.29, pp.6, 2016, https://doi.org/10.5351/KJAS.2016.29.6.1095
  3. An analysis of changes in the influence of GDP gap on inflation vol.26, pp.6, 2015, https://doi.org/10.7465/jkdi.2015.26.6.1377
  4. Estimation of Nonlinear Impulse Responses of Stock Indices by Asset Class vol.25, pp.2, 2012, https://doi.org/10.5351/KJAS.2012.25.2.239