2019
DOI: 10.1080/00949655.2019.1607346
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Robust rank-based variable selection in double generalized linear models with diverging number of parameters under adaptive Lasso

Abstract: Supplementary materials in this section are entirely based on the simulations study. All results and from different percent of contamination and the different sample size are summarized though the figures below. Figures 1, 2 3, and 4 summarizes the results in of scenario 1 in which the design space was contaminated using a normal contamination.

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Cited by 3 publications
(3 citation statements)
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“…The central idea of this type of method is to replace the minimized loss function based on the penalization of least squares, minimizing the sum of the loss and penalty functions. The classical penalty functions include the least absolute shrinkage and selection operator (Lasso), (12) smoothly clipped absolute deviation (SCAD) penalty, (13) Adaptive Lasso, (14) and Elastic Net. (15) However, with regard to variable screening methods that utilize a punishment function, the calculation is difficult and sometimes impossible for p >> n. In response to this problem, Cai and Lv (16) proposed a Dantzig screening method.…”
Section: Related Researchmentioning
confidence: 99%
“…The central idea of this type of method is to replace the minimized loss function based on the penalization of least squares, minimizing the sum of the loss and penalty functions. The classical penalty functions include the least absolute shrinkage and selection operator (Lasso), (12) smoothly clipped absolute deviation (SCAD) penalty, (13) Adaptive Lasso, (14) and Elastic Net. (15) However, with regard to variable screening methods that utilize a punishment function, the calculation is difficult and sometimes impossible for p >> n. In response to this problem, Cai and Lv (16) proposed a Dantzig screening method.…”
Section: Related Researchmentioning
confidence: 99%
“…The HNBR is a valuable tool for assessing the source of overdispersion. It belongs to the double-generalized linear models (DGLMs) or vectorgeneralized linear models (VGLMs), which are very useful in fitting more complex and potentially realistic models [15][16][17][18]. However, it appears that there is no study on selecting the dispersion explanation variables in the HNBR model.…”
Section: Introductionmentioning
confidence: 99%
“…HNBR is a valuable tool for assessing the source of overdispersion. It belongs to the double generalized linear models (DGLMs) or vector generalized linear models (VGLMs), which is very useful in fitting more complex and potentially realistic models (Yee, 2015;Nguelifack and Kemajou-Brown, 2019). However, little work has been done to select the dispersion explanation variables.…”
Section: Introductionmentioning
confidence: 99%