A new mortality model based on a mixture distribution function is proposed. We mix a half-normal distribution with a generalization of the skew-normal distribution. As a result, we get a six-parameter distribution function that has a good fit with a wide variety of mortality patterns. This mixture model is fitted to several mortality data schedules and compared with the Siler (five-parameter) and Heligman-Pollard (eight-parameter) models. Our proposal serves as a convenient compromise between the Heligman-Pollard model (which ensures a good fit with data but is often overparameterized) and the Siler model (which is more compact but fails to capture 'accident humps').
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