We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
ASYMPTOTIC DISTRIBUTIONS OF SEMIPARAMETRIC MAXIMUM LIKELIHOOD ESTIMATORS WITH ESTIMATING EQUATIONS FOR GROUP-CENSORED DATA.
- Authors
Chen, Di; Jye-Chyi Lu; Shu Chuan Lin
- Abstract
Semiparametric maximum likelihood estimation with estimating equations (SMLE) is more flexible than traditional methods; it has fewer restrictions on distributions and regression models. The required information about distribution and regression structures is incorporated in estimating equations of the SMLE to improve the estimation quality of non-parametric methods. The likelihood of SMLE for censored data involves complicated implicit functions without closed-form expressions, and the first derivatives of the log-profile-likelihood cannot be expressed as summations of independent and identically distributed random variables; it is challenging to derive asymptotic properties of the SMLE for censored data. For group-censored data, the paper shows that all the implicit functions are well defined and obtains the asymptotic distributions of the SMLE for model parameters and lifetime distributions. With several examples the paper compares the SMLE, the regular non-parametric likelihood estimation method and the parametric MLEs in terms of their asymptotic efficiencies, and illustrates application of SMLE. Various asymptotic distributions of the likelihood ratio statistics are derived for testing the adequacy of estimating equations and a partial set of parameters equal to some known values.
- Subjects
ASYMPTOTES; GENERALIZED estimating equations; STATISTICAL correlation; ESTIMATION theory; MATHEMATICAL statistics
- Publication
Australian & New Zealand Journal of Statistics, 2005, Vol 47, Issue 2, p173
- ISSN
1369-1473
- Publication type
Other
- DOI
10.1111/j.1467-842X.2005.00382.x