Penalized likelihood regression for generalized linear models with non-quadratic penalties

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Penalized likelihood regression for generalized linear models with nonquadratic penalties

One popular method for fitting a regression function is regularization: minimize an objective function which enforces a roughness penalty in addition to coherence with the data. This is the case when formulating penalized likelihood regression for exponential families. Most smoothing methods employ quadratic penalties, leading to linear estimates, and are in general incapable of recovering disc...

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ژورنال

عنوان ژورنال: Annals of the Institute of Statistical Mathematics

سال: 2009

ISSN: 0020-3157,1572-9052

DOI: 10.1007/s10463-009-0242-4