Canonical variate regression

Biostatistics

Volumn 17, Issue 3, 2016, Pages 468-483

  Luo, Chongliang ^a   Liu, Jin ^b   Dey, Dipak K ^a   Chen, Kun ^a  

^a UNIVERSITY OF CONNECTICUT   (United States)

^b DUKE NUS MEDICAL SCHOOL   (Singapore)

Author keywords

Canonical correlation analysis; Integrative analysis; Reduced rank regression; Supervised learning

Indexed keywords

ALCOHOLISM; ALGORITHM; ANIMAL; BODY WEIGHT; GENETICS; HUMAN; MOUSE; REGRESSION ANALYSIS; STATISTICAL ANALYSIS; STATISTICAL MODEL; SUPERVISED MACHINE LEARNING;

ALCOHOLISM; ALGORITHMS; ANIMALS; BODY WEIGHT; DATA INTERPRETATION, STATISTICAL; HUMANS; MICE; MODELS, STATISTICAL; REGRESSION ANALYSIS; SUPERVISED MACHINE LEARNING;

EID: 84977097839     PISSN: 14654644     EISSN: 14684357     Source Type: Journal    
DOI: 10.1093/biostatistics/kxw001     Document Type: Article

References (26)

1. BÍEMONT, C. (2010). From genotype to phenotype. What do Epigenetics and Epigenomics tell us. Heredity 105, 1-3.
2. BOYD, S., PARIKH, N., CHU, E., PELEATO, B. AND ECKSTEIN, J. (2011). Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends in Machine Learning 3, 1-122.
3. BOYD, S. AND VANDENBERGHE, L. (2004). Convex Optimization. NY, USA: Cambridge University Press.
4. CARROLL, L., VOISEY, J. AND VAN DAAL, A. (2004). Mouse models of obesity. Clinics in Dermatology 22, 345-349.
5. CERVINO, A. C., LI, G., EDWARDS, S., ZHU, J., LAURIE, C., TOKIWA, G., LUM, P. Y., WANG, S., CASTELLINI, L. W., LUSIS, A. J., et al. (2005). Integrating QTL and high-density SNP analyses in mice to identify Insig2 as a susceptibility gene for plasma cholesterol levels. Genomics 86, 505-517.
6. CHARTRAND, R. AND WOHLBERG, B. (2013). A nonconvex ADMM algorithm for group sparsity with sparse groups. Proceedings of IEEE Conference on Acoustics, Speech, and Signal Processing, pp. 6009-6013. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6638818.
7. CHEN, J., BUSHMAN, F. D., LEWIS, J. D., WU, G. AND LI, H. (2013). Structure-constrained sparse canonical correlation analysis with an application to microbiome data analysis. Biostatistics 14, 244-258.
8. DUEZ, H. AND STAELS, B. (2010). Nuclear receptors linking circadian rhythms and cardiometabolic control. Arteriosclerosis, Thrombosis, and Vascular Biology 30, 1529-1534.
9. ENGELBREGT, M. J., VAN WEISSENBRUCH, M. M., POPP-SNIJDERS, C., LIPS, P. AND DELEMARRE-VAN DE WAAL, H. A. (2001). Body mass index, body composition, and leptin at onset of puberty in male and female rats after intrauterine growth retardation and after early postnatal food restriction. Pediatric Research 50, 474-478.
10. GOWER, J. C. AND DIJKSTERHUIS, G. B. (2004) Procrustes Problems, Volume 3. Oxford: Oxford University Press.
11. GROSS, S. AND TIBSHIRANI, R. (2015). Collaborative regression. Biostatistics 16, 326-338.
12. HINES, I. N., HARTWELL, H. J., FENG, Y., THEVE, E. J., HALL, G. A., HASHWAY, S., CONNOLLY, J., FECTEAU, M., FOX, J. G. AND ROGERS, A. B. (2011). Insulin resistance and metabolic hepatocarcinogenesis with parent-of-origin effects in A ?B mice. The American Journal of Pathology 179, 2855-2865.
13. HOTELLING, H. (1936). Relations between two sets of variates. Biometrika 28, 321-377.
14. HUANG, J., BREHENY, P. AND MA, S. (2012). A selective review of group selection in high dimensional models. Statistical Science 27, 481-499.
15. KO, J.-H., LEE, T. J., PARK, C.-S., JANG, E.-H., OH, Y.-M. AND KANG, J.-H. (2008). Enhanced activity of flavincontaining monooxygenase in human subjects with high body mass index and in obese mice fed a high-fat diet. Molecular & Cellular Toxicology 4, 5-10.
16. PARKHOMENKO, E., TRITCHLER, D. AND BEYENE, J. (2009). Sparse canonical correlation analysis with application to genomic data integration. Statistical Applications in Genetics and Molecular Biology 8, 1-34.
17. REINSEL, G. C. AND VELU, P. (1998) Multivariate Reduced-rank Regression. New York: Springer.
18. THUM, A., MONCHGESANG, S., WESTPHAL, L., LÜBKEN, T., ROSAHL, S., NEUMANN, S. AND POSCH, S. (2014). Supervised penalized canonical correlation analysis. Preprint, 2014, arXiv:1405.1534.
19. TIBSHIRANI, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B 58, 267-288.
20. VISSCHER, P.M., BROWN, M. A., MCCARTHY, M. I. AND YANG, J. (2012). Five years ofGWAS discovery. The American Journal of Human Genetics 90, 7-24.
21. WANG, S., YEHYA, N., SCHADT, E. E., WANG, H., DRAKE, T. A. AND LUSIS, A. J. (2006). Genetic and genomic analysis of a fat mass trait with complex inheritance reveals marked sex specificity. PLoS Genetics 2, e15.
22. WITTEN, D. AND TIBSHIRANI, R. J. (2009). Extensions of sparse canonical correlation analysis with applications to genomic data. Statistical Applications in Genetics and Molecular Biology 8, 1-27.
23. WITTEN, D. M., TIBSHIRANI, R. AND HASTIE, T. (2009). A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics 10, 515-534.
24. YUAN, M. AND LIN, Y. (2006). Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society Series B 68, 49-67.
25. ZHANG, H., WANG, F., XU, H., LIU, Y., LIU, J., ZHAO, H. AND GELERNTER, J. (2014). Differentially co-expressed genes in postmortem prefrontal cortex of individuals with alcohol use disorders: influence on alcohol metabolism-related pathways. Human Genetics 133, 1383-1394.
26. ZOU, H. AND HASTIE, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society Series B 67, 301-320.