Abstract
For likelihood-based regression contexts, including generalized linear models, this paper presents a boosting algorithm for local constant quasi-likelihood estimators. Its advantages are the following: (a) the one-boosted estimator reduces bias in local constant quasi-likelihood estimators without increasing the order of the variance, (b) the boosting algorithm requires only one-dimensional maximization at each boosting step and (c) the resulting estimators can be written explicitly and simply in some practical cases.
Original language | English |
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Pages (from-to) | 235-248 |
Number of pages | 14 |
Journal | Annals of the Institute of Statistical Mathematics |
Volume | 62 |
Issue number | 2 |
DOIs | |
Publication status | Published - Apr 2010 |
Keywords
- Bias reduction
- Generalized linear models
- Kernel regression
- L Boosting
- Local quasi-likelihood
- Nadaraya - Watson estimator
ASJC Scopus subject areas
- Statistics and Probability