Spline nonparametric quasi-likelihood regression within the frame of the accelerated failure time model
The accelerated failure time model provides direct physical interpretation for right censored data. However, the homogeneity of variance assumption of the log transformed data does not always hold. In this paper, we propose using a generalized linear model for right censored data in which we relax the homogeneity assumption. A new semiparametric analysis method is proposed for this model. The method uses nonparametric quasi-likelihood in which the variance function is estimated by polynomial spline regression. This is based on squared residuals from an initial model fit. The rate of convergence of the nonparametric variance function estimator is derived. It is shown that the regression coefficient estimators are asymptotically normally distributed. Simulations show that for finite samples the proposed nonparametric quasi-likelihood method performs well. The new method is illustrated with one dataset.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:56:y:2012:i:9:p:2675-2687. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei)
If references are entirely missing, you can add them using this form.