  Given a gene expression data array of a list of bladder cancer patients with
their tumor states, it may be difficult to determine which genes can operate as
disease markers when the array is large and possibly contains outliers and
missing data. An additional difficulty is that observations (tumor states) in
the regression problem are discrete ones. In this article, we solve these
problems on concrete data using first a clustering approach, followed by Least
Absolute Shrinkage and Selection Operator (LASSO) estimators in a nonlinear
regression problem involving discrete variables, as described in the brand-new
research work of Plan and Vershynin. Gene markers of the most severe tumor
state are finally provided using the proposed approach.
