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Inference on Mixtures Under Tail Restrictions

Marc Henry, Koen Jochmans and Bernard Salanié ()

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Abstract: Two-component mixtures are nonparametrically identified under tail-dominance conditions on the component distributions if a source of variation is available that affects the mixing proportions but not the component distributions. We motivate these restrictions through several examples. One interesting example is a location model where the location parameter is subject to classical measurement error. The identification analysis suggests very simple closed-form estimators of the component distributions and mixing proportions based on ratios of intermediate quantiles. We derive their asymptotic properties using results on tail empirical processes, and we provide simulation evidence on their finite-sample performance.

Keywords: mixture model; nonparametric identification and estimation; tail empirical process (search for similar items in EconPapers)
Date: 2014-01
Note: View the original document on HAL open archive server: https://hal-sciencespo.archives-ouvertes.fr/hal-01053810
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Related works:
Working Paper: Inference on Mixtures Under Tail Restrictions (2015) Downloads
Working Paper: Inference on Mixtures Under Tail Restrictions (2015) Downloads
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