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A. P. Dempster, N. M. Laird and D. B. Rubin, “Maximum Likelihood from Incomplete Data via the EM Algorithm (with Discussion),” Journal of the Royal Statistical Society, Series B, Vol. 39, 1977, pp. 1-38.

has been cited by the following article:

• TITLE: Finite Mixture of Heteroscedastic Single-Index Models

  AUTHORS: Peng Zeng

  KEYWORDS: EM Algorithm; Finite Mixture Model; Heterogeneity; Heteroscedasticity; Local Linear Smoothing; Single-Index Model

  JOURNAL NAME: Open Journal of Statistics, Vol.2 No.1, January 19, 2012

  ABSTRACT: In many applications a heterogeneous population consists of several subpopulations. When each subpopulation can be adequately modeled by a heteroscedastic single-index model, the whole population is characterized by a finite mixture of heteroscedastic single-index models. In this article, we propose an estimation algorithm for fitting this model, and discuss the implementation in detail. Simulation studies are used to demonstrate the performance of the algorithm, and a real example is used to illustrate the application of the model.