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• W3206105606 abstract "Scientific advances in electronics and computers have led to data collection at faster rates and with higher accuracy, which has encouraged many researchers to develop a new methodology to analyze the huge amount of this data. In the biomedical field, there was an urgent need for statistical procedures capable of analyzing and interpreting big amounts of data. It is very difficult in current variables selection methods to deal with situations where the number of covariates understudy is very high. sure independent screening technique reduces the data from ultra-high to high dimensional data. It is also very difficult for variables selection methods, such as lasso, for example, to deal with situations of high correlations between the variables under study. In this paper, we propose to use the elastic net for a confirmed independent assay with the Cox proportional hazard model to address the high correlations and then select the important variables influencing the model. In order to test our proposed method, we ran a simulation and real data were taken to identify important and influencing genes." @default.
• W3206105606 created "2021-10-25" @default.
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• W3206105606 date "2021-01-01" @default.
• W3206105606 modified "2023-09-25" @default.
• W3206105606 title "Sure independent screening elastic net for ultra-high dimensional survival data" @default.
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• W3206105606 doi "https://doi.org/10.1063/5.0069137" @default.
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