### Abstract

Lingua originale | English |
---|---|

pagine (da-a) | 563-577 |

Numero di pagine | 15 |

Rivista | Biometrika |

Volume | 103 |

Stato di pubblicazione | Published - 2016 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Statistics and Probability
- Mathematics(all)
- Agricultural and Biological Sciences (miscellaneous)
- Agricultural and Biological Sciences(all)
- Statistics, Probability and Uncertainty
- Applied Mathematics

### Cita questo

*Biometrika*,

*103*, 563-577.

**A differential-geometric approach to generalized linear models with grouped predictors.** / Mineo, Angelo; Augugliaro, Luigi; Wit, Ernst C.

Risultato della ricerca: Article

*Biometrika*, vol. 103, pagg. 563-577.

}

TY - JOUR

T1 - A differential-geometric approach to generalized linear models with grouped predictors

AU - Mineo, Angelo

AU - Augugliaro, Luigi

AU - Wit, Ernst C.

PY - 2016

Y1 - 2016

N2 - We propose an extension of the differential-geometric least angle regression method to per- form sparse group inference in a generalized linear model. An efficient algorithm is proposed to compute the solution curve. The proposed group differential-geometric least angle regression method has important properties that distinguish it from the group lasso. First, its solution curve is based on the invariance properties of a generalized linear model. Second, it adds groups of variables based on a group equiangularity condition, which is shown to be related to score statis- tics. An adaptive version, which includes weights based on the Kullback–Leibler divergence, improves its variable selection features and is shown to have oracle properties when the number of predictors is fixed.

AB - We propose an extension of the differential-geometric least angle regression method to per- form sparse group inference in a generalized linear model. An efficient algorithm is proposed to compute the solution curve. The proposed group differential-geometric least angle regression method has important properties that distinguish it from the group lasso. First, its solution curve is based on the invariance properties of a generalized linear model. Second, it adds groups of variables based on a group equiangularity condition, which is shown to be related to score statis- tics. An adaptive version, which includes weights based on the Kullback–Leibler divergence, improves its variable selection features and is shown to have oracle properties when the number of predictors is fixed.

UR - http://hdl.handle.net/10447/193784

UR - http://biomet.oxfordjournals.org/content/103/3/563.abstract

M3 - Article

VL - 103

SP - 563

EP - 577

JO - Biometrika

JF - Biometrika

SN - 0006-3444

ER -