## Abstract

In regression modeling, often a restriction that regression coefficients are non-negative is faced. The problem of model selection in non-negative generalized linear models (NNGLM) is considered using lasso, where regression coefficients in the linear predictor are subject to non-negative constraints. Thus, non-negatively constrained regression coefficient estimation is sought by maximizing the penalized likelihood (such as the l_{1}-norm penalty). An efficient regularization path algorithm is proposed for generalized linear models with non-negative regression coefficients. The algorithm uses multiplicative updates which are fast and simultaneous. Asymptotic results are also developed for the constrained penalized likelihood estimates. Performance of the proposed algorithm is shown in terms of computational time, accuracy of solutions and accuracy of asymptotic standard deviations.

Original language | English |
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Pages (from-to) | 289-299 |

Number of pages | 11 |

Journal | Computational Statistics and Data Analysis |

Volume | 101 |

DOIs | |

Publication status | Published - 1 Sep 2016 |

## Keywords

- Generalized linear models
- Lasso
- Elastic net
- l(1)-norm penalty
- Regularization path
- Non-negativity constraints

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