Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/72374
Título: | A robust sparce linear approach for contamined data |
Autor(es): | Shahriari, Shirin Faria, Susana Gonçalves, A. Manuela |
Palavras-chave: | Jackknife Outlier detection Robust variable selection Sparsity |
Data: | 28-Mar-2019 |
Editora: | Taylor & Francis |
Revista: | Communications in Statistics - Simulation and Computation |
Resumo(s): | A challenging problem in a linear regression model is to select a parsimonious model which is robust to the presence of contamination in the data. In this paper, we present a sparse linear approach which detects outliers by using a highly robust regression method. The model with optimal predictive ability as measured by the median absolute deviation of the prediction errors on JackKnife subsets is used to detect outliers. The performance of the proposed method is evaluated on a simulation study with a different type of outliers and high leverage points and also on a real data set. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/72374 |
DOI: | 10.1080/03610918.2019.1588304 |
ISSN: | 0361-0918 |
e-ISSN: | 1532-4141 |
Versão da editora: | https://doi.org/10.1080/03610918.2019.1588304 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | CMAT - Artigos em revistas com arbitragem / Papers in peer review journals |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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A robust sparse linear approach for contaminated data.pdf | Article | 1,87 MB | Adobe PDF | Ver/Abrir |