### Abstract

Language | English |
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Title of host publication | 60th ISI World Statistics Congress |

Subtitle of host publication | proceedings |

Place of Publication | The Hague, The Netherlands |

Publisher | International Statistical Institute |

Pages | 2642-2647 |

Number of pages | 6 |

ISBN (Print) | 9789073592353 |

Publication status | Published - 2015 |

Event | World Statistics Congress of the International Statistical Institute (60th : 2015) - Rio de Janeiro Duration: 26 Jul 2015 → 31 Jul 2015 |

### Conference

Conference | World Statistics Congress of the International Statistical Institute (60th : 2015) |
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City | Rio de Janeiro |

Period | 26/07/15 → 31/07/15 |

### Fingerprint

### Cite this

*60th ISI World Statistics Congress: proceedings*(pp. 2642-2647). The Hague, The Netherlands: International Statistical Institute.

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*60th ISI World Statistics Congress: proceedings.*International Statistical Institute, The Hague, The Netherlands, pp. 2642-2647, World Statistics Congress of the International Statistical Institute (60th : 2015), Rio de Janeiro, 26/07/15.

**Model selection curves for survival analysis with accelerated failure time models.** / Karami, J. H.; Luo, K.; Fung, T.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › Research › peer-review

TY - GEN

T1 - Model selection curves for survival analysis with accelerated failure time models

AU - Karami,J. H.

AU - Luo,K.

AU - Fung,T.

PY - 2015

Y1 - 2015

N2 - Many model selection processes involve minimizing a loss function of the data over a set of models. A recent introduced approach is model selection curves, in which the selection criterion is expressed as a function of penalty multiplier in a linear (mixed) model or generalized linear model. In this article, we have adopted the model selection curves for accelerated failure time (AFT) models of survival data. In our simulation study, it was found that for data with small sample size and high proportion of censoring, the model selection curves approach outperformed the traditional model selection criteria, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). In other situations with respect to sample size and proportion of censoring, the model selection curves correctly identify the true model whether it is a full or reduced model. Moreover, through bootstrapping, it was shown that the model selection curves can be used to enhance the model selection process in AFT models.

AB - Many model selection processes involve minimizing a loss function of the data over a set of models. A recent introduced approach is model selection curves, in which the selection criterion is expressed as a function of penalty multiplier in a linear (mixed) model or generalized linear model. In this article, we have adopted the model selection curves for accelerated failure time (AFT) models of survival data. In our simulation study, it was found that for data with small sample size and high proportion of censoring, the model selection curves approach outperformed the traditional model selection criteria, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). In other situations with respect to sample size and proportion of censoring, the model selection curves correctly identify the true model whether it is a full or reduced model. Moreover, through bootstrapping, it was shown that the model selection curves can be used to enhance the model selection process in AFT models.

KW - log-likelihood function

KW - penalty function

KW - penalty multiplier

KW - survival data

M3 - Conference proceeding contribution

SN - 9789073592353

SP - 2642

EP - 2647

BT - 60th ISI World Statistics Congress

PB - International Statistical Institute

CY - The Hague, The Netherlands

ER -