On the foundations of parameter estimation for generalized partial linear models with B-splines and continuous optimization
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Tarih
2010
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Pergamon-Elsevier Science Ltd
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Generalized linear models are widely used in statistical techniques. As an extension, generalized partial linear models utilize semiparametric methods and augment the usual parametric terms with a single nonparametric component of a continuous covariate. In this paper, after a short introduction, we present our model in the generalized additive context with a focus on the penalized maximum likelihood and the penalized iteratively reweighted least squares (P-IRLS) problem based on B-splines, which is attractive for nonparametric components. Then, we approach solving the P-IRLS problem using continuous optimization techniques. They have come to constitute an important complementary approach, alternative to the penalty methods, with flexibility for choosing the penalty parameter adaptively. In particular, we model and treat the constrained P-IRIS problem by using the elegant framework of conic quadratic programming. The method is illustrated using a small numerical example. (C) 2010 Elsevier Ltd. All rights reserved.
Açıklama
Anahtar Kelimeler
Generalized Partial Linear Models, Maximum Likelihood, Penalty Methods, Conic Quadratic Programming, Cmars
Kaynak
Computers & Mathematics With Applications
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
60
Sayı
1
