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Semiparametric mixture of experts with unspecifed gate network
Journal of the Korean Data & Information Science Society 2017;28:685-95
Published online May 31, 2017
© 2017 Korean Data & Information Science Society.

Dahai Jung1 · Byungtae Seo2

12Department of Statistics, Sungkyunkwan University
Correspondence to: Byungtae Seo
Associate Professor, Department of Statistics, Sungkyunkwan University, Seoul 03063, Korea. E-mail:
Received March 22, 2017; Revised May 17, 2017; Accepted May 18, 2017.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
The traditional mixture of experts (ME) modeled the gate network using a certain parametric function. However, if the assumed parametric function does not properly reflect the true nature, the prediction strength of ME would become weak. For example, the parametric ME often uses logistic or multinomial logistic models for the network model. However, this could be very misleading if the true nature of the data is quite different from those models. Although, in this case, we may develop more flexible parametric models by extending the model at hand, we will never be free from such misspecification problems. In order to alleviate such weakness of the parametric ME, we propose to use the semi-parametric mixture of experts (SME) in which the gate network is estimated in a non-parametrical way. Based on this, we compared the performance of the SME with those of ME and neural networks via several simulation experiments and real data examples.
Keywords : EM algorithm, mixture of experts, neural network, semiparametric models.