  In the present paper we address the real-time detection problem of a
change-point in the coefficients of a linear model with the possibility that
the model errors are asymmetrical and that the explanatory variables number is
large. We build test statistics based on the cumulative sum (CUSUM) of the
expectile function derivatives calculated on the residuals obtained by the
expectile and adaptive LASSO expectile estimation methods. The asymptotic
distribution of these statistics are obtained under the hypothesis that the
model does not change. Moreover, we prove that they diverge when the model
changes at an unknown observation. The asymptotic study of the test statistics
under these two hypotheses allows us to find the asymptotic critical region and
the stopping time, that is the observation where the model will change. The
empirical performance is investigated by a comparative simulation study with
other statistics of CUSUM type. Two examples on real data are also presented to
demonstrate its interest in practice.
