Maximum likelihood estimation in a model with interval data

It is suggested that in some situations, observations for random variables should be collected in the form of intervals. In this paper, the unknown parameters in a bivariate normal model are estimated based on a set of point and interval observations via the maximum likelihood approach. The Newton-Raphson algorithm is used to find the estimates, and asymptotic properties of the estimator are provided. Monte Carlo studies are conducted to study the performance of the estimator. An example based on real-life data is presented to demonstrate the practical applicability of the method.