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Given a gene expression data array of a list of bladder cancer patients withtheir tumor states, it may be difficult to determine which genes can operate asdisease markers when the array is large and possibly contains outliers andmissing data. An additional difficulty is that observations (tumor states) inthe regression problem are discrete ones. In this article, we solve theseproblems on concrete data using first a clustering approach, followed by LeastAbsolute Shrinkage and Selection Operator (LASSO) estimators in a nonlinearregression problem involving discrete variables, as described in the brand-newresearch work of Plan and Vershynin. Gene markers of the most severe tumorstate are finally provided using the proposed approach.

Using the LASSO for gene selection in bladder cancer data