Parameter estimation in geometric process with Weibull distribution

AYDOĞDU H., ŞENOĞLU B., Kara M.

APPLIED MATHEMATICS AND COMPUTATION, cilt.217, sa.6, ss.2657-2665, 2010 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 217 Sayı: 6
  • Basım Tarihi: 2010
  • Doi Numarası: 10.1016/j.amc.2010.08.003
  • Dergi Adı: APPLIED MATHEMATICS AND COMPUTATION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.2657-2665
  • Anahtar Kelimeler: Taylor series, Geometric process, Modified likelihood, Asymptotic normality, Consistency, Efficiency, Monte Carlo simulation, MODIFIED MAXIMUM-LIKELIHOOD, REGRESSION
  • Ankara Üniversitesi Adresli: Evet

Özet

We consider geometric process (GP) when the distribution of the first occurrence time of an event is assumed to be Weibull. Explicit estimators of the parameters in GP are derived by using the method of modified maximum likelihood (MML) proposed by Tiku [24]. Asymptotic distributions and consistency properties of these estimators are obtained. We show that our estimators are more efficient than the widely used modified moment (MM) estimators via Monte Carlo simulation study. Further, two real life examples are given at the end of the paper. (C) 2010 Elsevier Inc. All rights reserved.