The Least Absolute Shrinkage and Selection Operator or LASSO [Tib96] is a technique for model selection and estimation in linear regression models. The LASSO minimizes the residual sum of squares subject to the sum of the absolute value of coefficients being less than a constant. Tibshirani applies a quadratic opti- mization problem with 2p linear equality constraints to obtain the LASSO solution for a fixed value of the tuning parameter t. In particular, the LASSO can be very useful in the analysis of microarray data in which the number of genes (predictors) is much larger than the number of sample observations. In this paper, we apply the LASSO methodology to a microarray study in breast cancer.
LASSO estimators in linear regression for microarray data
POLLICE, Alessio;
2006-01-01
Abstract
The Least Absolute Shrinkage and Selection Operator or LASSO [Tib96] is a technique for model selection and estimation in linear regression models. The LASSO minimizes the residual sum of squares subject to the sum of the absolute value of coefficients being less than a constant. Tibshirani applies a quadratic opti- mization problem with 2p linear equality constraints to obtain the LASSO solution for a fixed value of the tuning parameter t. In particular, the LASSO can be very useful in the analysis of microarray data in which the number of genes (predictors) is much larger than the number of sample observations. In this paper, we apply the LASSO methodology to a microarray study in breast cancer.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
