Estimation of the generalized process capability index C pyk based on bias-corrected maximum-likelihood estimators for the generalized inverse Lindley distribution and bootstrap confidence intervals


Journal of Statistical Computation and Simulation, 2021 (Journal Indexed in SCI Expanded) identifier

  • Publication Type: Article / Article
  • Volume:
  • Publication Date: 2021
  • Doi Number: 10.1080/00949655.2021.1879081
  • Title of Journal : Journal of Statistical Computation and Simulation


© 2021 Informa UK Limited, trading as Taylor & Francis Group.In this paper, we are interested in estimating the generalized process capability index ((Formula presented.)) proposed by Maiti et al. [On generalizing process capability indices. Qual Technol Quant Manag. 2010;7(3):279–300], when the underlying distribution is the generalized inverse Lindley (GIL) distribution. We estimate parameters of the GIL distribution using maximum likelihood (ML), bias-corrected maximum-likelihood (BCML) and bootstrap bias-corrected maximum-likelihood (BBCML) methodologies. (Formula presented.) are obtained using proposed estimators. Bootstrap confidence intervals called standard bootstrap (SB), percentile bootstrap (PB) and bias-corrected percentile bootstrap (BCPB) (Formula presented.) are constructed based on the estimators of (Formula presented.). We compare efficiencies of the parameter estimators and the performance of ML, BCML and BBCML based Cpyk via an extensive Monte Carlo simulation study. A simulation study is also described to compare the coverage probabilities (CP) and average lengths (AL) of SB, PB and BCPB confidence intervals for proposed (Formula presented.). Finally, two real datasets are analysed for illustrative purposes.