Residuals are the differences between the observed and predicted values of a model. In the context of lifetime data, residuals can be used to assess the fit of a survival model to the data and to check the assumptions of the model. There are different types of residuals for survival models, such as Cox-Snell residuals, Martingale residuals, deviance residuals, etc. Each type of residual has its own advantages and disadvantages and may require some transformation or adjustment to be useful.

One possible terminology for residuals of fitting for lifetime data is residual life. Residual life is the remaining lifetime of an individual or a system after a certain time point. Residual life can be estimated from a survival model and can be used to measure the reliability or risk of failure of an individual or a system. Residual life can also be expressed as a function of time, such as median residual life or mean residual life.