Uncertain stochastic ridge estimation in partially linear regression models with elliptically distributed errors

Roozbeh, Mahdi; Hamzah, Nor Aishah

doi:10.1080/02331888.2020.1764558

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2023

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Cited by 5 publications

(2 citation statements)

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“…Consider the following stochastic restrictions, this can refer to Roozbeh et al 26 and Roozbeh and Hamzah 27 : For the mixed estimator, KL estimator, OLS estimator, mixed ridge estimator and mixed KL estimator proposed in this paper. The MSE is presented in Table 2 .…”

Section: Numerical Example and Simulation Studymentioning

confidence: 99%

On the mixed Kibria–Lukman estimator for the linear regression model

Chen

2022

Sci Rep

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This paper considers a linear regression model with stochastic restrictions,we propose a new mixed Kibria–Lukman estimator by combining the mixed estimator and the Kibria–Lukman estimator.This new estimator is a general estimation, including OLS estimator, mixed estimator and Kibria–Lukman estimator as special cases. In addition, we discuss the advantages of the new estimator based on MSEM criterion, and illustrate the theoretical results through examples and simulation analysis.

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Section: Numerical Example and Simulation Studymentioning

confidence: 99%

On the mixed Kibria–Lukman estimator for the linear regression model

Chen

2022

Sci Rep

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“…Hu [20] introduced a ridge estimator by the parametric component β. Liu et al [8] introduced a PCR estimator in partially linear models. For more references, one can refer to Roozbeh [21], Roozbeh et al [22], Akdeniz and Roozbeh [23], Roozbeh et al [24], Roozbeh and Hanzah [25], and Wei and Wang [26].…”

Section: Introductionmentioning

confidence: 99%