Asymptotic equivalence for a model of independent non identically distributed observations
Authors:
- Michael JähnischMJMichael Jähnisch
- Michael NussbaumMNMichael Nussbaum
- 1 March 2003
- journal article
- research article
- Published by Walter de Gruyter GmbH in Statistics & Decisions
- Vol. 21 (3-2003) , 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.Keywords
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