Clustering Effects in Unreplicated Factorial Experiments
In: Communications in statistics. Simulation and computation, Band 42, Heft 9, S. 1998-2007
ISSN: 1532-4141
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In: Communications in statistics. Simulation and computation, Band 42, Heft 9, S. 1998-2007
ISSN: 1532-4141
In: Communications in statistics. Simulation and computation, Band 42, Heft 7, S. 1587-1595
ISSN: 1532-4141
In: Statistical papers, Band 61, Heft 5, S. 2203-2219
ISSN: 1613-9798
In: Statistica Neerlandica: journal of the Netherlands Society for Statistics and Operations Research, Band 68, Heft 4, S. 276-292
ISSN: 1467-9574
In recent years, we have seen an increased interest in the penalized likelihood methodology, which can be efficiently used for shrinkage and selection purposes. This strategy can also result in unbiased, sparse, and continuous estimators. However, the performance of the penalized likelihood approach depends on the proper choice of the regularization parameter. Therefore, it is important to select it appropriately. To this end, the generalized cross‐validation method is commonly used. In this article, we firstly propose new estimates of the norm of the error in the generalized linear models framework, through the use of Kantorovich inequalities. Then these estimates are used in order to derive a tuning parameter selector in penalized generalized linear models. The proposed method does not depend on resampling as the standard methods and therefore results in a considerable gain in computational time while producing improved results. A thorough simulation study is conducted to support theoretical findings; and a comparison of the penalized methods with theL1, the hard thresholding, and the smoothly clipped absolute deviation penalty functions is performed, for the cases of penalized Logistic regression and penalized Poisson regression. A real data example is being analyzed, and a discussion follows. © 2014 The Authors. Statistica Neerlandica © 2014 VVS.
In: Statistical papers, Band 56, Heft 1, S. 121-145
ISSN: 1613-9798
In: Communications in statistics. Simulation and computation, Band 42, Heft 3, S. 552-569
ISSN: 1532-4141
In: Communications in statistics. Simulation and computation, Band 42, Heft 9, S. 1961-1969
ISSN: 1532-4141