Yazar "Aydin D." seçeneğine göre listele

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    Choice of smoothing parameter for kernel type ridge estimators in semiparametric regression models
    (National Statistical Institute, 2021) Yilmaz E.; Yuzbasi B.; Aydin D.
    This paper concerns kernel-type ridge estimators of parameters in a semiparametric model. These estimators are a generalization of the well-known Speckman’s approach based on kernel smoothing method. The most important factor in achieving this smoothing method is the selection of the smoothing parameter. In the literature, many selection criteria for comparing regression models have been produced. We will focus on six selection criterion improved version of Akaike information criterion (AICc), generalized cross-validation (GCV), Mallows’ Cp criterion, risk estimation using classical pilots (RECP), Bayes information criterion (BIC), and restricted maximum likelihood (REML). Real and simulated data sets are considered to illustrate the key ideas in the paper. Thus, suitable selection criterion are provided for optimum smoothing parameter selection. © 2021, National Statistical Institute. All rights reserved.
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    Modified ridge type estimator in partially linear regression models and numerical comparisons
    (American Scientific Publishers, 2016) Aydin D.; Yüzbaşi B.; Ahmed S.E.
    In this article, we introduce a modified ridge type estimator for the vector of parameters in a partially linear model. This estimator is a generalization of the well-known Speckman's approach and is based on smoothing splines method. Most important in the implementation of this method is the choice of the smoothing parameter. Many Criteria of selecting smoothing parameters such as improved version of Akaike information criterion (AICc), generalized cross-validation (GCV), crossvalidation (CV), Mallows' Cp criterion, risk estimation using classical pilots (REC) and Bayes information criterion (BIC) are developed in literature. In order to illustrate the ideas in the paper, a real data example and a Monte Carlo simulation study are carried out. Thus, the appropriate selection criteria are provided for a suitable smoothing parameter selection. © Copyright 2016 American Scientific Publishers All rights reserved.