This paper proposes a bias reduction of the coefficients' estimator for linear regression models when observations are randomly censored and the error distribution is unknown. The proposed bias correction is applied to the weighted least squares estimator proposed by Stute [28] [W. Stute: Consistent estimation under random censorship when covariables are present. J. Multivariate Anal. \emph{45} (1993), 89-103.], and it is based on model-based bootstrap resampling techniques that also allow us to work with censored data. Our bias-corrected estimator proposal is evaluated and its behavior assessed in simulation studies concluding that both the bias and the mean square error are reduced with the new proposal.
The paper presents results of mechanical testing of soft tissues (arterial walls) under biaxial stress conditions and analysis of the influence of some factors, such as specimen location, reconditioning, etc. Soft tissues are pseudoelastic materials, modelled mostly as hyperelastic, either isotropic or anisotropic ones. Therefore multiaxial (biaxial) mechanical tests are required for a credible identification of their mechanical parameters. As living tissue proporties change with time after excision and exhibit also viscoelastic behaviour, a much more specialized equipment is needed to perform biaxial tests of soft tissues. A test rig for biaxial tests is presented in the paper and a pronounced influence of stress state character on the specimen behaviour during several first cycles is analyzed. and Obsahuje seznam literatury