Bag of little bootstraps for massive and distributed longitudinal data
Linear mixed models are widely used for analyzing longitudinal datasets, and the inference for variance component parameters relies on the bootstrap method. However, health systems and technology companies routinely generate massive longitudinal datasets that make the traditional bootstrap method in...
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| Published in: | Statistical analysis and data mining Vol. 15; no. 3; pp. 314 - 321 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Wiley Subscription Services, Inc., A Wiley Company
01.06.2022
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| ISSN: | 1932-1864, 1932-1872 |
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| Abstract | Linear mixed models are widely used for analyzing longitudinal datasets, and the inference for variance component parameters relies on the bootstrap method. However, health systems and technology companies routinely generate massive longitudinal datasets that make the traditional bootstrap method infeasible. To solve this problem, we extend the highly scalable bag of little bootstraps method for independent data to longitudinal data and develop a highly efficient Julia package MixedModelsBLB.jl. Simulation experiments and real data analysis demonstrate the favorable statistical performance and computational advantages of our method compared to the traditional bootstrap method. For the statistical inference of variance components, it achieves 200 times speedup on the scale of 1 million subjects (20 million total observations), and is the only currently available tool that can handle more than 10 million subjects (200 million total observations) using desktop computers. |
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| AbstractList | Linear mixed models are widely used for analyzing longitudinal datasets, and the inference for variance component parameters relies on the bootstrap method. However, health systems and technology companies routinely generate massive longitudinal datasets that make the traditional bootstrap method infeasible. To solve this problem, we extend the highly scalable bag of little bootstraps method for independent data to longitudinal data and develop a highly efficient Julia package
MixedModelsBLB.jl.
Simulation experiments and real data analysis demonstrate the favorable statistical performance and computational advantages of our method compared to the traditional bootstrap method. For the statistical inference of variance components, it achieves 200 times speedup on the scale of 1 million subjects (20 million total observations), and is the only currently available tool that can handle more than 10 million subjects (200 million total observations) using desktop computers. Linear mixed models are widely used for analyzing longitudinal datasets, and the inference for variance component parameters relies on the bootstrap method. However, health systems and technology companies routinely generate massive longitudinal datasets that make the traditional bootstrap method infeasible. To solve this problem, we extend the highly scalable bag of little bootstraps method for independent data to longitudinal data and develop a highly efficient Julia package MixedModelsBLB.jl. Simulation experiments and real data analysis demonstrate the favorable statistical performance and computational advantages of our method compared to the traditional bootstrap method. For the statistical inference of variance components, it achieves 200 times speedup on the scale of 1 million subjects (20 million total observations), and is the only currently available tool that can handle more than 10 million subjects (200 million total observations) using desktop computers. Linear mixed models are widely used for analyzing longitudinal datasets, and the inference for variance component parameters relies on the bootstrap method. However, health systems and technology companies routinely generate massive longitudinal datasets that make the traditional bootstrap method infeasible. To solve this problem, we extend the highly scalable bag of little bootstraps method for independent data to longitudinal data and develop a highly efficient Julia package MixedModelsBLB.jl. Simulation experiments and real data analysis demonstrate the favorable statistical performance and computational advantages of our method compared to the traditional bootstrap method. For the statistical inference of variance components, it achieves 200 times speedup on the scale of 1 million subjects (20 million total observations), and is the only currently available tool that can handle more than 10 million subjects (200 million total observations) using desktop computers. Linear mixed models are widely used for analyzing longitudinal datasets, and the inference for variance component parameters relies on the bootstrap method. However, health systems and technology companies routinely generate massive longitudinal datasets that make the traditional bootstrap method infeasible. To solve this problem, we extend the highly scalable bag of little bootstraps method for independent data to longitudinal data and develop a highly efficient Julia package MixedModelsBLB.jl. Simulation experiments and real data analysis demonstrate the favorable statistical performance and computational advantages of our method compared to the traditional bootstrap method. For the statistical inference of variance components, it achieves 200 times speedup on the scale of 1 million subjects (20 million total observations), and is the only currently available tool that can handle more than 10 million subjects (200 million total observations) using desktop computers.Linear mixed models are widely used for analyzing longitudinal datasets, and the inference for variance component parameters relies on the bootstrap method. However, health systems and technology companies routinely generate massive longitudinal datasets that make the traditional bootstrap method infeasible. To solve this problem, we extend the highly scalable bag of little bootstraps method for independent data to longitudinal data and develop a highly efficient Julia package MixedModelsBLB.jl. Simulation experiments and real data analysis demonstrate the favorable statistical performance and computational advantages of our method compared to the traditional bootstrap method. For the statistical inference of variance components, it achieves 200 times speedup on the scale of 1 million subjects (20 million total observations), and is the only currently available tool that can handle more than 10 million subjects (200 million total observations) using desktop computers. |
| Author | Zhou, Xinkai Zhou, Jin J. Zhou, Hua |
| AuthorAffiliation | 2 Department of Medicine, University of California, Los Angeles, California, USA 3 Department of Computational Medicine, University of California, Los Angeles, California, USA 1 Department of Biostatistics, University of California, Los Angeles, California, USA |
| AuthorAffiliation_xml | – name: 3 Department of Computational Medicine, University of California, Los Angeles, California, USA – name: 1 Department of Biostatistics, University of California, Los Angeles, California, USA – name: 2 Department of Medicine, University of California, Los Angeles, California, USA |
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| Cites_doi | 10.18637/jss.v059.i09 10.1007/s10107-004-0559-y 10.2337/dc15-0598 10.18637/jss.v067.i01 10.1046/j.1365-2869.2003.00337.x 10.1007/978-0-387-76721-5_1 10.1214/aos/1176344552 10.1080/01621459.1987.10478472 10.1111/rssb.12050 10.1007/978-1-4612-1554-7 10.1111/j.1467‐9868.2004.00438.x 10.1016/S0140-6736(10)60576-4 10.1007/978-1-4757-2545-2 10.1007/0-387-30065-1_4 10.1111/biom.13506 |
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| SubjectTerms | bags of little bootstraps big data Data analysis Datasets EMR linear mixed models longitudinal data parallel and distributed computing Personal computers Statistical analysis Statistical inference Statistical methods Variance |
| Title | Bag of little bootstraps for massive and distributed longitudinal data |
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