By Topic

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Wenxin Jiang ; Dept. of Stat., Northwestern Univ., Evanston, IL, USA ; Tanner, M.A.

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+xTβ) 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

Published in:

Information Theory, IEEE Transactions on  (Volume:46 ,  Issue: 3 )