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The weight of penalty optimization for ridge regression

Zuliana, SU and Perperoglou, A (2016) The weight of penalty optimization for ridge regression. In: UNSPECIFIED, ? - ?.

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Abstract

© Springer International Publishing Switzerland 2016. A method of weight optimization is introduced when fitting penalized ridge regression models. A penalty term added to a likelihood may be viewed in the light of a hierarchical likelihood. Under this context a method to estimate the variance of a random effect in a mixed model can be employed to obtain an estimate of the penalization weight.We review the theory of ridge penalties from a Bayesian point of view and show how an algorithm for estimating the variance of a random effect can be combined with hierarchical likelihood. The method is compared with other commonly used methods to obtain a penalty weight, such as leave-one-out cross validation, generalized cross validation, penalized quasi-likelihood methods and principal components estimation. Simulation studies are performed to compare the different approaches. For each of the methods we use packages already publicly available in the statistical software R.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: Published proceedings: Studies in Classification, Data Analysis, and Knowledge Organization
Subjects: H Social Sciences > HA Statistics
Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Mathematical Sciences, Department of
Depositing User: Jim Jamieson
Date Deposited: 05 Dec 2016 21:14
Last Modified: 23 Jan 2019 05:16
URI: http://repository.essex.ac.uk/id/eprint/18342

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