Sum of Bernoulli Mixtures: Beyond Conditional Independence

• Main Authors: Taehan Bae, Ian Iscoe
• Format: Article
• Language: English
• Published: Hindawi Limited 2014-01-01
• Series: Journal of Probability and Statistics
• Online Access: http://dx.doi.org/10.1155/2014/838625

We consider the distribution of the sum of Bernoulli mixtures under a general dependence structure. The level of dependence is measured in terms of a limiting conditional correlation between two of the Bernoulli random variables. The conditioning event is that the mixing random variable is larger than a threshold and the limit is with respect to the threshold tending to one. The large-sample distribution of the empirical frequency and its use in approximating the risk measures, value at risk and conditional tail expectation, are presented for a new class of models which we call double mixtures. Several illustrative examples with a Beta mixing distribution, are given. As well, some data from the area of credit risk are fit with the models, and comparisons are made between the new models and also the classical Beta-binomial model.