Asymptotic equivalence for a model of independent non identically distributed observations

Jähnisch Michael and Nussbaum Michael

Statistics & Risk Modeling, 2003, vol. 21, issue 3, 197-218

Abstract: It is shown that a nonparametric model of independent non identically distributed observations on the unit interval can be approximated, in the sense of Le Cam´s Δ-distance, by a bivariate Gaussian white noise model. The parameter space is a smoothness class of conditional densities uniformly bounded away from zero on the unit square. The proof is based on coupling of likelihood processes via a functional Hungarian construction of the sequential empirical process and the Kiefer–Müller process.

Date: 2003
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DOI: 10.1524/stnd.21.3.197.23430

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