A computational approach test for comparing two linear regression models with unequal variances

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Yazici M. E., GÖKPINAR F., GÖKPINAR E., EBEGİL M., ÖZDEMİR Y. A.

HACETTEPE JOURNAL OF MATHEMATICS AND STATISTICS, cilt.50, sa.6, ss.1756-1772, 2021 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 50 Sayı: 6
  • Basım Tarihi: 2021
  • Doi Numarası: 10.15672/hujms.784623
  • Dergi Adı: HACETTEPE JOURNAL OF MATHEMATICS AND STATISTICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, zbMATH, TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.1756-1772
  • Anahtar Kelimeler: &nbsp, Chow test, computational approach test, parametric bootstrap test, heteroscedasticity regression models, LARGE-SAMPLE INFERENCE, DISTURBANCE VARIANCES, CHOW TEST, EQUALITY, COEFFICIENTS, SETS, RATIO
  • Gazi Üniversitesi Adresli: Evet

Özet

In this study, a new testing procedure is proposed to compare two linear regression models based on a computational approach test when the variances are not assumed equal. This method is based on restricted maximum likelihood estimators and some simple computational steps. To assess performance of the proposed test, it was compared with some existing tests in terms of power and type I error rate of the test. The simulation study reveals that the proposed test is a better alternative than some existing tests in most cases considered. Besides, an illustration of the proposed test was given by using a sample dataset.