### Abstract

Original language | English |
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Pages (from-to) | n/a-n/a |

Journal | Risk Analysis |

DOIs | |

Publication status | Published - 2016 |

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### Keywords

- Kullback-Leibler loss, logistic regression, maximum likelihood estimate, uncertainty

### Cite this

*Risk Analysis*, n/a-n/a. https://doi.org/10.1111/risa.12558

**Estimating the Probability of Rare Events Occurring Using a Local Model Averaging.** / Chen, Jin-Hua; Chen, Chun-Shu; Huang, Meng-Fan; Lin, Hung-Chih.

Research output: Contribution to journal › Article

*Risk Analysis*, pp. n/a-n/a. https://doi.org/10.1111/risa.12558

}

TY - JOUR

T1 - Estimating the Probability of Rare Events Occurring Using a Local Model Averaging

AU - Chen, Jin-Hua

AU - Chen, Chun-Shu

AU - Huang, Meng-Fan

AU - Lin, Hung-Chih

PY - 2016

Y1 - 2016

N2 - In statistical applications, logistic regression is a popular method for analyzing binary data accompanied by explanatory variables. But when one of the two outcomes is rare, the estimation of model parameters has been shown to be severely biased and hence estimating the probability of rare events occurring based on a logistic regression model would be inaccurate. In this article, we focus on estimating the probability of rare events occurring based on logistic regression models. Instead of selecting a best model, we propose a local model averaging procedure based on a data perturbation technique applied to different information criteria to obtain different probability estimates of rare events occurring. Then an approximately unbiased estimator of Kullback-Leibler loss is used to choose the best one among them. We design complete simulations to show the effectiveness of our approach. For illustration, a necrotizing enterocolitis (NEC) data set is analyzed.

AB - In statistical applications, logistic regression is a popular method for analyzing binary data accompanied by explanatory variables. But when one of the two outcomes is rare, the estimation of model parameters has been shown to be severely biased and hence estimating the probability of rare events occurring based on a logistic regression model would be inaccurate. In this article, we focus on estimating the probability of rare events occurring based on logistic regression models. Instead of selecting a best model, we propose a local model averaging procedure based on a data perturbation technique applied to different information criteria to obtain different probability estimates of rare events occurring. Then an approximately unbiased estimator of Kullback-Leibler loss is used to choose the best one among them. We design complete simulations to show the effectiveness of our approach. For illustration, a necrotizing enterocolitis (NEC) data set is analyzed.

KW - Kullback-Leibler loss, logistic regression, maximum likelihood estimate, uncertainty

KW - Kullback-Leibler loss

KW - logistic regression

KW - maximum likelihood estimate

KW - uncertainty

U2 - 10.1111/risa.12558

DO - 10.1111/risa.12558

M3 - Article

SP - n/a-n/a

JO - Risk Analysis

JF - Risk Analysis

SN - 0272-4332

ER -