On the asymptotic normality of hierarchical mixtures-of-experts for generalized linear models

Wenxin Jiang, Martin A. Tanner

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

In the class of hierarchical mixtures-of-experts (HME) models, 'experts' in the exponential family with generalized linear mean functions of the form ψ (α + χT β) are mixed, according to a set of local weights called the 'gating functions' depending on the predictor x. Here ψ(·) is the inverse link function. We provide regularity conditions on the experts and on the gating functions under which the maximum-likelihood method in the large sample limit produces a consistent and asymptotically normal estimator of the mean response. The regularity conditions are validated for Poisson, gamma, normal, and binomial experts.

Original languageEnglish (US)
Pages (from-to)1005-1013
Number of pages9
JournalIEEE Transactions on Information Theory
Volume46
Issue number3
DOIs
StatePublished - May 2000

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences

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