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

Abstract

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.