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- W2600728971 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." @default.
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- W2600728971 date "2017-09-13" @default.
- W2600728971 modified "2023-09-27" @default.
- W2600728971 title "Semiparametric mixture of additive regression models" @default.
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- W2600728971 doi "https://doi.org/10.1080/03610926.2017.1310243" @default.
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