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**Confidence sets for split points in decision trees.**
*(English)*
Zbl 1117.62037

Summary: We investigate the problem of finding confidence sets for split points in decision trees (CART). Our main results establish the asymptotic distribution of the least squares estimators and some associated residual sum of squares statistics in a binary decision tree approximation to a smooth regression curve. Cube-root asymptotics with non-normal limit distributions are involved. We study various confidence sets for the split point, one calibrated using the subsampling bootstrap, and others calibrated using plug-in estimates of some nuisance parameters. The performance of the confidence sets is assessed in a simulation study. A motivation for developing such confidence sets comes from the problem of phosphorus pollution in the Everglades. Ecologists have suggested that split points provide a phosphorus threshold at which biological imbalance occurs, and the lower endpoint of the confidence set may be interpreted as a level that is protective of the ecosystem. This is illustrated using data from a Duke University Wetlands Center phosphorus dosing study in the Everglades.

### MSC:

62G08 | Nonparametric regression and quantile regression |

62E20 | Asymptotic distribution theory in statistics |

62G15 | Nonparametric tolerance and confidence regions |

62P12 | Applications of statistics to environmental and related topics |

62G20 | Asymptotic properties of nonparametric inference |

### Keywords:

CART; change-point estimation; cube-root asymptotics; empirical processes; logistic regression; Poisson regression; split point
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\textit{M. Banerjee} and \textit{I. W. McKeague}, Ann. Stat. 35, No. 2, 543--574 (2007; Zbl 1117.62037)

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