Summary: An approach to produce a model for the data generating distribution is the well-known maximum entropy method. In this approach, the partial knowledge about the data generating distribution is formulated in terms of a set of information constraints, usually moment constraints, and the inference is based on the model that maximizes Shannon’s entropy under these constraints.
We investigate several problems of hazard rate function estimation based on the maximum entropy principle. The potential applications include developing several classes of the maximum entropy distributions which can be used to model different data-generating distributions that satisfy certain information constraints on the hazard rate function.