Fisher information in a competing risks model with inhomogeneous random interval censoring
Abstract
Fisher information is a fundamental concept in the theory of statistical inference and plays a crucial role in the estimation of parameters in statistical models. It represents a measure of the informativeness of observed data with respect to the unknown parameters of the model. In situations involving random censoring, where some observations may be incomplete or censored, calculating Fisher information becomes a complex task that has garnered significant attention from researchers. In this paper, we compute and investigate the Fisher information in a competing risks model under inhomogeneous random interval censoring. The results obtained indicate that under certain conditions, Fisher information retains its properties even in the presence of incomplete observations, which is crucial for improving the accuracy of parameter estimation in statistical models.