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Comparison of Lasso Type Estimators for High-Dimensional Data
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 Title & Authors
Comparison of Lasso Type Estimators for High-Dimensional Data
Kim, Jaehee;
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This paper compares of lasso type estimators in various high-dimensional data situations with sparse parameters. Lasso, adaptive lasso, fused lasso and elastic net as lasso type estimators and ridge estimator are compared via simulation in linear models with correlated and uncorrelated covariates and binary regression models with correlated covariates and discrete covariates. Each method is shown to have advantages with different penalty conditions according to sparsity patterns of regression parameters. We applied the lasso type methods to Arabidopsis microarray gene expression data to find the strongly significant genes to distinguish two groups.
Adaptive Lasso;elastic net;fused lasso;high-dimensional data;lasso;ridge;
 Cited by
Benjamin, Y. and Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing, Journal of Royal Statistical Society B, 57, 289-300.

Buhlmann, P. and van de Geer, S. (2011). Statistics for High-Dimensional Data, Springer, New York.

Efron, B., Hastie, T., Johnstone, I. and Tibshirani, R. (2004). Least angle regression, Annals of Statistics, 32, 407-451. crossref(new window)

Efron, B. and Tibshirani, R. (1993). An Introduction to the Bootstrap, Chapman and Hall, London.

Friedman, J., Hasti, T. and Tibshirani, R. (2001). The Elements of Statistical Learning, Springer, New York.

Hoerl, A. E. and Kennard, R. W. (1970). Ridge regression: Applications to nonorthogonal problems, Technometrics, 12, 69-82. crossref(new window)

Kim, T. H., Hauser, F., Ha, T., Xue, S., Boehmer, M., Nishimura, N., Munemasa, S., Hubbard, K., Peine, N., Lee, B. H., Lee, S., Robert, N., Parker, J. E. and Schroeder, J. I. (2011). Chemical genetics reveals negative regulation of abscisic acid signaling by a plant immune response pathway, Current Biology, 21, 990-997. crossref(new window)

Kyung, M., Gill, J., Ghosh, M. and Casella, G. (2010). Penalized regression, standard errors, and Bayesian lassos, Bayesian Analysis, 5, 369-412. crossref(new window)

Park, T. and Casella, G. (2008). The Bayesian lasso, Journal of the American Statistical Association, 103, 681-686. crossref(new window)

Stein, C. (1981). Estimation of the mean of a multivariate normal distribution, The Annals of Statistics, 9, 1135-1151. crossref(new window)

Tibshirani, R. (1996). Regression shrinkage and selection via the lasso, Journal of Royal Statiatical Society B, 58, 267-288.

Tibshirani, R., Saunders, M., Rosset, S., Zhu, J. and Knight, K. (2005). Sparsity and smoothness via the fused lasso, Journal of Royal Statistical Society B, 67, 91-108. crossref(new window)

Zou, H. (2006). The adaptive lasso and its oracle properties, Journal of the American Statistical Association, 101, 1418-1429. crossref(new window)

Zou, H. and Hastie, T. (2005). Regularization and variable selection via the elastic net, Journal of Royal Statistical Society B, 67, 301-320. crossref(new window)