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In this work, we propose a stochastic method which gives an estimated solution for a linear calibration problem with α−mixing random data. We establish exponential inequalities of Fuk Nagaev type, for the probability of the distance between the approximate solutions and the exact one. Furthermore, we build a confidence domain for the so mentioned exact solution. To check the validity of our results, a numerical example is proposed.

Almost complete convergence for the sequence of approximate solutions in linear calibration problem with α-mixing random data