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

机译:广义线性模型的专家层级混合的渐近正态性

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In the class of hierarchical mixtures-of-experts (HME) models, "experts" in the exponential family with generalized linear mean functions of the form /spl psi/(/spl alpha/+x/sup T//spl beta/) are mixed, according to a set of local weights called the "gating functions" depending on the predictor x. Here /spl psi/(/spl middot/) 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.
机译:在分级专家混合(HME)模型的类别中,指数族中的“专家”具有形式为/ spl psi /(/ spl alpha / + x / sup T // spl beta /)的广义线性均值函数根据一组称为预测函数x的局部权重,将权重混合。 / spl psi /(/ spl middot /)是反向链接函数。我们为专家和选通函数提供了规则性条件,在这种条件下,大样本限制中的最大似然法可得出均值响应的一致且渐近正态估计。正则性条件已针对Poisson,γ,正态和二项式专家进行了验证。

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