On Regularisation Methods for Analysis of High Dimensional Data
High dimensional data are rapidly growing in many domains due to the development of technological advances which helps collect data with a large number of variables to better understand a given phenomenon of interest. Particular examples appear in genomics, fMRI data analysis, large-scale healthcare...
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| Ngôn ngữ: | en_US |
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Springer Nature
2020
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| Truy cập trực tuyến: | https://doi.org/10.1007/s40745-019-00209-4 http://tailieuso.tlu.edu.vn/handle/DHTL/9409 |
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oai:localhost:DHTL-94092020-09-14T09:40:21Z On Regularisation Methods for Analysis of High Dimensional Data Sirimongkolkasem, Tanin Drikvandi, Reza De-biased lasso High dimensional data Linear regression model High dimensional data are rapidly growing in many domains due to the development of technological advances which helps collect data with a large number of variables to better understand a given phenomenon of interest. Particular examples appear in genomics, fMRI data analysis, large-scale healthcare analytics, text/image analysis and astronomy. In the last two decades regularisation approaches have become the methods of choice for analysing such high dimensional data. This paper aims to study the performance of regularisation methods, including the recently proposed method called de-biased lasso, for the analysis of high dimensional data under different sparse and non-sparse situations. Our investigation concerns prediction, parameter estimation and variable selection. We particularly study the effects of correlated variables, covariate location and effect size which have not been well investigated. We find that correlated data when associated with important variables improve those common regularisation methods in all aspects, and that the level of sparsity can be reflected not only from the number of important variables but also from their overall effect size and locations. The latter may be seen under a non-sparse data structure. We demonstrate that the debiased lasso performs well especially in low dimensional data, however it still suffers from issues, such as multicollinearity and multiple hypothesis testing, similar to the classical regression methods. https://doi.org/10.1007/s40745-019-00209-4 2020-09-14T09:37:32Z 2020-09-14T09:37:32Z 2019 BB https://doi.org/10.1007/s40745-019-00209-4 http://tailieuso.tlu.edu.vn/handle/DHTL/9409 en_US Annals of Data Science (2019), Volume 6, Issue 4, pp 737–763 application/pdf Springer Nature |
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Trường Đại học Thủy Lợi |
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en_US |
| topic |
De-biased lasso High dimensional data Linear regression model |
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De-biased lasso High dimensional data Linear regression model Sirimongkolkasem, Tanin On Regularisation Methods for Analysis of High Dimensional Data |
| description |
High dimensional data are rapidly growing in many domains due to the development of technological advances which helps collect data with a large number of variables to better understand a given phenomenon of interest. Particular examples appear in genomics, fMRI data analysis, large-scale healthcare analytics, text/image analysis and astronomy. In the last two decades regularisation approaches have become the methods of choice for analysing such high dimensional data. This paper aims to study the performance of regularisation methods, including the recently proposed method called de-biased lasso, for the analysis of high dimensional data under different sparse and non-sparse situations. Our investigation concerns prediction, parameter estimation and
variable selection. We particularly study the effects of correlated variables, covariate location and effect size which have not been well investigated. We find that correlated data when associated with important variables improve those common regularisation methods in all aspects, and that the level of sparsity can be reflected not only from the number of important variables but also from their overall effect size and locations. The latter may be seen under a non-sparse data structure. We demonstrate that the debiased lasso performs well especially in low dimensional data, however it still suffers from issues, such as multicollinearity and multiple hypothesis testing, similar to the classical regression methods. |
| author2 |
Drikvandi, Reza |
| author_facet |
Drikvandi, Reza Sirimongkolkasem, Tanin |
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BB |
| author |
Sirimongkolkasem, Tanin |
| author_sort |
Sirimongkolkasem, Tanin |
| title |
On Regularisation Methods for Analysis of High Dimensional Data |
| title_short |
On Regularisation Methods for Analysis of High Dimensional Data |
| title_full |
On Regularisation Methods for Analysis of High Dimensional Data |
| title_fullStr |
On Regularisation Methods for Analysis of High Dimensional Data |
| title_full_unstemmed |
On Regularisation Methods for Analysis of High Dimensional Data |
| title_sort |
on regularisation methods for analysis of high dimensional data |
| publisher |
Springer Nature |
| publishDate |
2020 |
| url |
https://doi.org/10.1007/s40745-019-00209-4 http://tailieuso.tlu.edu.vn/handle/DHTL/9409 |
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