Advanced search
Publication Cover
Communications in Statistics - Simulation and Computation Volume 50, 2021 - Issue 4
Submit an article Journal homepage
923
Views
30
CrossRef citations to date
0
Altmetric
Original Articles

A random subset implementation of weighted quantile sum (WQSRS) regression for analysis of high-dimensional mixtures

Paul Curtina Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York City, NY, USACorrespondence[email protected]
View further author information
,
Joshua Kelloggb Department of Chemistry and Biochemistry, University of North Carolina at Greensboro, Greensboro, NC, USAView further author information
,
Nadja Cechb Department of Chemistry and Biochemistry, University of North Carolina at Greensboro, Greensboro, NC, USAView further author information
&
Chris Genningsa Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York City, NY, USAView further author information
Pages 1119-1134 | Received 19 Jun 2018, Accepted 19 Jan 2019, Published online: 11 Mar 2019

References

  • Bello, G. A., G. G. Dumancas, and C. Gennings. 2015. Development and validation of a clinical risk-assessment tool predictive of all-cause mortality. Bioinformatics and Biology Insights 9:1–10. doi: 10.4137/Bbi.S30172.
  • Carrico, C., C. Gennings, D. C. Wheeler, and P. Factor-Litvak. 2015. Characterization of weighted quantile sum regression for highly correlated data in a risk analysis setting. Journal of Agricultural, Biological, and Environmental Statistics 20 (1):100–20. doi: 10.1007/s13253-014-0180-3.
  • Gennings, C., C. Carrico, P. Factor-Litvak, N. Krigbaum, P. M. Cirillo, and B. A. Cohn. 2013. A cohort study evaluation of maternal PCB exposure related to time to pregnancy in daughters. Environmental Health 12 (1):66. doi: 10.1186/1476-069x-12-66.
  • Hastie, T., R. Tibshirani, and J. Friendman. 2009. The elements of statistical learning. Vol.2, Springer Series in Statistics. New York: Springer.
  • Ho, T. K. 1998. The random subspace method for constructing decision forests. Ieee Transactions on Pattern Analysis and Machine Intelligence 20 (8):832–44.
  • Kaiser, H. F., and K. Dickman. 1962. Sample and population score matrices and sample Correlation-Matrices from an arbitrary population correlation matrix. Psychometrika 27 (2):179–82. doi: 10.1007/BF02289635.
  • Kellogg, J. J., D. A. Todd, J. M. Egan, H. A. Raja, N. H. Oberlies, O. M. Kvalheim, and N. B. Cech. 2016. Biochemometrics for natural products research: Comparison of data analysis approaches and application to identification of bioactive compounds. Journal of Natural Products 79 (2):376–86. doi: 10.1021/acs.jnatprod.5b01014.
  • Mielniczuk, J., and P. Teisseyre. 2014. Using random subspace method for prediction and variable importance assessment in linear regression. Computational Statistics & Data Analysis 71:725–42. doi: 10.1016/j.csda.2012.09.018.
  • Mitro, S. D., L. S. Birnbaum, B. L. Needham, and A. R. Zota. 2016. Cross-sectional associations between exposure to persistent organic pollutants and leukocyte telomere length among US adults in NHANES, 2001-2002. Environmental Health Perspectives 124 (5):651–8. doi: 10.1289/ehp.1510187.
  • Scinicariello, F., and M. C. Buser. 2015. Polychlorinated biphenyls and leukocyte telomere length: An analysis of NHANES 1999-2002. EBioMedicine 2 (12):1974–9. doi: 10.1016/j.ebiom.2015.11.028.
  • Scinicariello, F., A. G. Feroe, and R. Attanasio. 2016. Urinary phthalates and leukocyte telomere length: An analysis of NHANES 1999-2002. Ebiomedicine 6:96–102. doi: 10.1016/j.ebiom.2016.02.027.
  • Song, L., P. Langfelder, and S. Horvath. 2013. Random generalized linear model: a highly accurate and interpretable ensemble predictor. BMC Bioinformatics 14:5. doi: 10.1186/1471-2105-14.
  • Stafoggia, M., S. Breitner, R. Hampel, and X. Basagana. 2017. Statistical approaches to address multi-pollutant mixtures and multiple exposures: The state of the science. Current Environmental Health Reports 4 (4):481–90. doi: 10.1007/s40572-017-0162-z.
  • Tu, Y. K., D. Gunnell, and M. S. Gilthorpe. 2008. Simpson’s paradox, Lord’s paradox, and suppression effects are the same phenomenon-the reversal paradox. Emerging Themes in Epidemiology 5:2.doi: 10.1186/1742-7622-5-2.
  • Zou, H., and T. Hastie. 2005. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 67 (5):768. doi: 10.1111/j.1467-9868.2005.00527.x.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Order Reprints Request Corporate Permissions

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

Request Academic Permissions

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.