A common problem in microarray cancer studies is how to identify and select the most informative marker genes whose expression levels are predictive of clinical or other outcomes of interest. A major constraint however, is that the expression levels of most of these genes are often collected on relatively few samples which makes the use of classical regression methods inappropriate for genes selection and the prediction of biological samples. A number of methods have been proposed in literature some of which are characterized by procedural complexities. In this book, a novel k sequential...