Linear contrasts in one-way classification AR(1) model with gamma innovations

ŞENOĞLU B., TÜRKER BAYRAK Ö.

HACETTEPE JOURNAL OF MATHEMATICS AND STATISTICS, cilt.45, sa.6, ss.1743-1754, 2016 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 45 Sayı: 6
  • Basım Tarihi: 2016
  • Doi Numarası: 10.15672/hjms.20164515996
  • Dergi Adı: HACETTEPE JOURNAL OF MATHEMATICS AND STATISTICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.1743-1754
  • Anahtar Kelimeler: Autoregressive model, linear contrasts, nonnormality, robustness, modified likelihood, gamma distribution, TIME-SERIES MODELS, AUTOREGRESSIVE MODELS, ESTIMATING PARAMETERS, NONNORMAL SITUATIONS, MAXIMUM-LIKELIHOOD, DESIGN
  • Ankara Üniversitesi Adresli: Evet

Özet

In this study, the explicit estimators of the model parameters in oneway classification AR(1) model with gamma innovations are derived by using modified maximum likelihood (MML) methodology. We also propose a new test statistic for testing linear contrasts. Monte Carlo simulation results show that the MML estimators have higher efficiencies than the traditional least squares (LS) estimators and the proposed test has much better power and robustness properties than the normal theory test.