### 抜粋

Non-concave penalized maximum likelihood methods are widely used because they are more efficient than the Lasso. They include a tuning parameter which controls a penalty level, and several information criteria have been developed for selecting it. While these criteria assure the model selection consistency, they have a problem in that there are no appropriate rules for choosing one from the class of information criteria satisfying such a preferred asymptotic property. In this paper, we derive an information criterion based on the original definition of the AIC by considering minimization of the prediction error rather than model selection consistency. Concretely speaking, we derive a function of the score statistic that is asymptotically equivalent to the non-concave penalized maximum likelihood estimator and then provide an estimator of the Kullback–Leibler divergence between the true distribution and the estimated distribution based on the function, whose bias converges in mean to zero.

元の言語 | 英語 |
---|---|

ページ（範囲） | 247-274 |

ページ数 | 28 |

ジャーナル | Annals of the Institute of Statistical Mathematics |

巻 | 71 |

発行部数 | 2 |

DOI | |

出版物ステータス | 出版済み - 4 1 2019 |

### All Science Journal Classification (ASJC) codes

- Statistics and Probability

## フィンガープリント AIC for the non-concave penalized likelihood method' の研究トピックを掘り下げます。これらはともに一意のフィンガープリントを構成します。

## これを引用

*Annals of the Institute of Statistical Mathematics*,

*71*(2), 247-274. https://doi.org/10.1007/s10463-018-0649-x