This work considers the problem of fitting functional models with sparsely and irregularly sampled data. It overcomes limitations state-of-the-art methods, which face major challenges in more complex non-linear models. Currently, many these cannot be consistently estimated unless number observed points per curve grows sufficiently quickly sample size, whereas, we show numerically that a modifie...

BACKGROUND Missing data on tumour stage information is a common problem in population-based cancer registries. Statistical analyses on the level of tumour stage may be biased, if no adequate method for handling of missing data is applied. In order to determine a useful way to treat missing data on tumour stage, we examined different imputation models for multiple imputation with chained equatio...