A Multivariate Variable Model with Possibility of Missing Data on a Stochastic Process

A joint model for multivariate responses with potentially non-random missing values on a stochastic process is proposed. A full likelihood-based approach that allows yielding maximum likelihood estimates of the model parameters is used. Sensitivity of the results to the assumptions is also investigated. A common way to investigate whether perturbations of model components influence key results of the analysis is to compare the results derived from the original and perturbed models using a general index of sensitivity (ISNI). The approach is illustrated by analyzing a finance data set