New estimating equation approaches with application in lifetime data analysis

Article

Abstract

Estimating equation approaches have been widely used in statistics inference. Important examples of estimating equations are the likelihood equations. Since its introduction by Sir R. A. Fisher almost a century ago, maximum likelihood estimation (MLE) is still the most popular estimation method used for fitting probability distribution to data, including fitting lifetime distributions with censored data. However, MLE may produce substantial bias and even fail to obtain valid confidence intervals when data size is not large enough or there is censoring data. In this paper, based on nonlinear combinations of order statistics, we propose new estimation equation approaches for a class of probability distributions, which are particularly effective for skewed distributions with small sample sizes and censored data. The proposed approaches may possess a number of attractive properties such as consistency, sufficiency and uniqueness. Asymptotic normality of these new estimators is derived. The construction of new estimation equations and their numerical performance under different censored schemes are detailed via Weibull distribution and generalized exponential distribution.

Keywords

Estimation equation Nonlinear combination of order statistics Asymptotic normality Weibull distribution Generalized exponential distribution 

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Copyright information

© The Institute of Statistical Mathematics, Tokyo 2012

Authors and Affiliations

  1. 1.Department of Mathematical SciencesBrunel UniversityLondonUK
  2. 2.Business SchoolShihezi UniversityWujiaquChina
  3. 3.Department of StatisticsZhejiang Gongshang UniversityHangzhouChina
  4. 4.Ecole Nationale de la Statistique et de l’Analyse de l’Information (Ensai)(CRES)BRUZ CedexFrance

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