In the context of microarray data, a common characteristic is that the number of parameter is greater than the number of samples (n p). Because of this feature, many existing methods, derived for the usual "small p and large n" problem, either cannot be applied or may not perform well. For the purpose of classification of tumor types in real and simulated microarray data using regularized and classification approaches, we have studied three regression methods, namely Least Absolute Shrinkage and Selection Operator (LASSO), ridge regression, elastic net and four classification methods namely...