This article proposes an adjusted empirical likelihood estimation (AMELE) method to model and analyze accelerated life testing data. This approach flexibly and rigorously incorporates distribution assumptions and regression structures by estimating equations within a semiparametric estimation framework. An efficient method is provided to compute the empirical likelihood estimates, and asymptotic properties are studied. Real-life examples and numerical studies demonstrate the advantage of the proposed methodology.
Copyright © 2011 Elsevier B.V.
The definitive version is available at: https://www.sciencedirect.com/science/article/pii/S0378375810002636
Wang, Ni, Jye-Chi Lu, Di Chen, and Paul H. Kvam. "Adjusted Empirical Likelihood Models with Estimating Equations for Accelerated Life Tests." Journal of Statistical Planning and Inference141, no. 1 (2011): 140-155. doi:10.1016/j.jspi.2010.05.016.
Wang, Ni; Lu, Jye-Chyi; Chen, Di; and Kvam, Paul H., "Adjusted Empirical Likelihood Models with Estimating Equations for Accelerated Life Tests" (2011). Math and Computer Science Faculty Publications. 204.