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Semiparametric mixture of additive regression models

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  • Yi Zhang
  • Qingle Zheng

Abstract

In this article, we propose a semiparametric mixture of additive regression models, in which the regression functions are additive and non parametric while the mixing proportions and variances are constant. Compared with the mixture of linear regression models, the proposed methodology is more flexible in modeling the non linear relationship between the response and covariate. A two-step procedure based on the spline-backfitted kernel method is derived for computation. Moreover, we establish the asymptotic normality of the resultant estimators and examine their good performance through a numerical example.

Suggested Citation

  • Yi Zhang & Qingle Zheng, 2018. "Semiparametric mixture of additive regression models," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(3), pages 681-697, February.
    • Handle: RePEc:taf:lstaxx:v:47:y:2018:i:3:p:681-697
    DOI: 10.1080/03610926.2017.1310243
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    Cited by:

    1. Sphiwe B. Skhosana & Salomon M. Millard & Frans H. J. Kanfer, 2023. "A Novel EM-Type Algorithm to Estimate Semi-Parametric Mixtures of Partially Linear Models," Mathematics, MDPI, vol. 11(5), pages 1-20, February.
    2. Marco Berrettini & Giuliano Galimberti & Saverio Ranciati, 2023. "Semiparametric finite mixture of regression models with Bayesian P-splines," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(3), pages 745-775, September.

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