REFERENCES
- Bach, F. (2010), “Self-Concordant Analysis for Logistic Regression,” Electronic Journal of Statistics, 4, 384–414.
- Baum, A., Akula, N., Cabanero, M., Cardona, I., Corona, W., Klemens, B., Schulze, T., Cichon, S., Rietschel, M., Nöthen, M., Georgi, A., Schumacher, J., Schwarz, M., Jamra, R A., Höfels, S., Propping, P., and Satagopan, J., NIMH Genetics Initiative Bipolar Disorder Consortium, and Detera-Wadleigh, S. D., Hardy, J., and McMahon, F. J. (2007), “A Genome-Wide Association Study Implicates Diacylglycerol Kinase Eta (DGKH) and Several Other Genes in the Etiology of Bipolar Disorder,” Molecular Psychiatry, 13, 197–207.
- Baum, A., Hamshere, M., Green, E., Cichon, S., Rietschel, M., Noethen, M., Craddock, N., and McMahon, F. (2008), “Meta-Analysis of Two Genome-Wide Association Studies of Bipolar Disorder Reveals Important Points of Agreement,” Molecular Psychiatry, 13, 466–467.
- Bickel, P.J., Ritov, Y., and Tsybakov, A.B. (2009), “Simultaneous Analysis of Lasso and Dantzig Selector,” The Annals of Statistics, 37, 1705–1732.
- Breiman, L., Friedman, J., Olshen, R., and Stone, C. (1984), Classification and Regression Trees, Boca Raton, FL: Chapman & Hall/CRC.
- Bunea, F. (2008), “Honest Variable Selection in Linear and Logistic Regression Models via ℓ1 and ℓ 1+ ℓ 2 Penalization,” Electronic Journal of Statistics, 2, 1153–1194.
- Bunea, F., and Barbu, A. (2009), “Dimension Reduction and Variable Selection in Case Control Studies via Regularized Likelihood Optimization,” Electronic Journal of Statistics, 3, 1257–1287.
- Bunea, F., Tsybakov, A., and Wegkamp, M. (2007), “Sparsity Oracle Inequalities for the Lasso,” Electronic Journal of Statistics, 1, 169–194.
- Cantor, R., Lange, K., and Sinsheimer, J. (2010), “Prioritizing GWAS Results: A Review of Statistical Methods and Recommendations for Their Application,” The American Journal of Human Genetics, 86, 6–22.
- Cho, S., Kim, H., Oh, S., Kim, K., and Park, T. (2009), “Elastic-Net Regularization Approaches for Genome-Wide Association Studies of Rheumatoid Arthritis,” BMC Proceedings, 3, S25.
- Collett, D. (2003), Modelling Binary Data, Boca Raton, FL: CRC Press.
- Craddock, N., and Forty, L. (2006), “Genetics of Affective (Mood) Disorders,” European Journal of Human Genetics, 14, 660–668.
- Efron, B., Hastie, T., Johnstone, I., and Tibshirani, R. (2004), “Least Angle Regression,” Annals of Statistics, 32, 407–451.
- Fan, J., and Lv, J. (2011), “Non-Concave Penalized Likelihood With NP-Dimensionality,” IEEE Transactions on Information Theory, 57, 5467–5484.
- Ferreira, M., O’Donovan, M., Meng, Y., Jones, I., Ruderfer, D., Jones, L., Fan, J., Kirov, G., Perlis, R., Green, E., et al. (2008), “Collaborative Genome-Wide Association Analysis Supports a Role for ANK3 and CACNA1C in Bipolar Disorder,” Nature Genetics, 40, 1056–1058.
- Friedman, J., Hastie, T., and Tibshirani, R. (2000), “Special Invited Paper. Additive Logistic Regression: A Statistical View of Boosting,” Annals of Statistics, 28, 337–374.
- ——— (2010), “Regularization Paths for Generalized Linear Models via Coordinate Descent,” Journal of Statistical Software, 33, 1–22.
- Jiang, Y., and Zhang, C.M. (2013), “High-Dimensional Regression and Classification Under a Class of Convex Loss Functions,” Statistics and Its Interface, 6, 285–299.
- Johnson, A., and O’Donnell, C. (2009), “An Open Access Database of Genome-Wide Association Results,” BMC Medical Genetics, 10, 6.
- Knight, K., and Fu, W. (2000), “Asymptotics for Lasso-Type Estimators,” The Annals of Statistics, 28, 1356–1378.
- Koltchinskii, V., Lounici, K., and Tsybakov, A.B. (2011), “Nuclear-Norm Penalization and Optimal Rates for Noisy Low-Rank Matrix Completion,” The Annals of Statistics, 39, 2302–2329.
- Kwemou, M. (2012), “Non-Asymptotic Oracle Inequalities for the Lasso and Group Lasso in High Dimensional Logistic Model,” arXiv preprint arXiv:1206.0710.
- McCullagh, P., and Nelder, J. (1989), Generalized Linear Models, Boca Raton, FL: Chapman & Hall/CRC.
- Meier, L., van der Geer, S., and Bühlmann, P. (2008), “The Group Lasso for Logistic Regression,” Journal of the Royal Statistical Society, Series B, 70, 53–71.
- Meinshausen, N., and Yu, B. (2009), “Lasso-Type Recovery of Sparse Representations for High-Dimensional Data,” Annals of Statistics, 37, 246–270.
- Merikangas, K., Akiskal, H., Angst, J., Greenberg, P., Hirschfeld, R., Petukhova, M., and Kessler, R. (2007), “Lifetime and 12-Month Prevalence of Bipolar Spectrum Disorder in the National Comorbidity Survey Replication,” Archives of General Psychiatry, 64, 543–552.
- Ollila, H., Soronen, P., Silander, K., Palo, O., Kieseppä, T., Kaunisto, M., Lönnqvist, J., Peltonen, L., Partonen, T., and Paunio, T. (2009), “Findings From Bipolar Disorder Genome-Wide Association Studies Replicate in a Finnish Bipolar Family-Cohort,” Molecular Psychiatry, 14, 351–353.
- Raskutti, G., Wainwright, M.J., and Yu, B. (2010), “Restricted Eigenvalue Properties for Correlated Gaussian Designs,” The Journal of Machine Learning Research, 11, 2241–2259.
- Rosset, S., and Zhu, J. (2007), “Piecewise Linear Regularized Solution Paths,” Annals of Statistics, 35, 1012–1030.
- Schapire, R., Rochery, M., Rahim, M., and Gupta, N. (2005), “Boosting With Prior Knowledge for Call Classification,” IEEE Transactions on Speech and Audio Processing, 13, 174–181.
- Scott, L., Muglia, P., Kong, X., Guan, W., Flickinger, M., Upmanyu, R., Tozzi, F., Li, J., Burmeister, M., Absher, D., et al. (2009), “Genome-Wide Association and Meta-Analysis of Bipolar Disorder in Individuals of European Ancestry,” Proceedings of the National Academy of Sciences, 106, 7501–7506.
- Sklar, P., Smoller, J., Fan, J., Ferreira, M., Perlis, R., Chambert, K., Nimgaonkar, V., McQueen, M., Faraone, S., Kirby, A., et al. (2008), “Whole-Genome Association Study of Bipolar Disorder,” Molecular Psychiatry, 13, 558–569.
- Smith, E., Bloss, C., Badner, J., Barrett, T., Belmonte, P., Berrettini, W., Byerley, W., Coryell, W., Craig, D., Edenberg, H., et al. (2009), “Genome-Wide Association Study of Bipolar Disorder in European American and African American Individuals,” Molecular Psychiatry, 14, 755–763.
- Tibshirani, R. (1996), “Regression Shrinkage and Selection via the Lasso,” Journal of the Royal Statistical Society, Series B, 58, 267–288.
- Geer, S.A. (2008), “High-Dimensional Generalized Linear Models and the Lasso,” The Annals of Statistics, 36, 614–645.
- van der Vaart, A.W., and Wellner, J.A. (1996), Weak Convergence and Empirical Processes: With Applications to Statistics, New York: Springer-Verlag.
- (2007), “Genome-Wide Association Study of 14,000 Cases of Seven Common Diseases and 3,000 Shared Controls,” Nature, 447, 661–678.
- Wu, T., Chen, Y., Hastie, T., Sobel, E., and Lange, K. (2009), “Genome-Wide Association Analysis by Lasso Penalized Logistic Regression,” Bioinformatics, 25, 714–721.
- Wu, T., and Lange, K. (2008), “Coordinate Descent Algorithms for Lasso Penalized Regression,” Annals of Applied Statistics, 2, 224–244.
- Wu, Y. (2011), “An Ordinary Differential Equation-Based Solution Path Algorithm,” Journal of Nonparametric Statistics, 23, 185–199.
- Zeggini, E., and Ioannidis, J. (2009), “Meta-Analysis in Genome-Wide Association Studies,” Pharmacogenomics, 10, 191–201.
- Zeggini, E., Scott, L., Saxena, R., Voight, B., Marchini, J., Hu, T., de Bakker, P., Abecasis, G., Almgren, P., Andersen, G., et al. (2008), “Meta-Analysis of Genome-Wide Association Data and Large-Scale Replication Identifies Additional Susceptibility Loci for Type 2 Diabetes,” Nature Genetics, 40, 638–645.
- Zhang, H., and Lu, W. (2007), “Adaptive Lasso for Cox’s Proportional Hazards Model,” Biometrika, 94, 691–703.
- Zhao, P., and Yu, B. (2006), “On Model Selection Consistency of Lasso,” Journal of Machine Learning Research, 7, 2541–2563.
- Zou, H. (2006), “The Adaptive Lasso and Its Oracle Properties,” Journal of the American Statistical Association, 101, 1418–1429.
- 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.