Liu-type shrinkage estimations in linear models

Yüzbaşi B., Asar Y., Ahmed S. E.

Statistics, cilt.10, sa.11, ss.1-25, 2022 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 10 Sayı: 11
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1080/02331888.2022.2055030
  • Dergi Adı: Statistics
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, ABI/INFORM, MathSciNet, zbMATH
  • Sayfa Sayıları: ss.1-25
  • Anahtar Kelimeler: Sub-model, full model, pretest and shrinkage estimation, penalty estimation, Monte Carlo simulation, VARIABLE SELECTION, REGRESSION, LIKELIHOOD, LASSO
  • İnönü Üniversitesi Adresli: Evet

Özet

In this study, we present the preliminary test, Stein-type and positive part Stein-type Liu estimators in the linear models when the parameter vector beta is partitioned into two parts, namely, the main effects beta and the nuisance effects beta(2) such that beta = (beta(1), beta(2)). We consider the case that a priori known or suspected set of the explanatory variables do not contribute to predict the response so that a sub-model may be enough for this purpose. Thus, the main interest is to estimate beta(1) when beta(2) is close to zero. Therefore, we investigate the performance of the suggested estimators asymptotically and via a Monte Carlo simulation study. Moreover, we present a real data example to evaluate the relative efficiency of the suggested estimators, where we demonstrate the superiority of the proposed estimators.