Geodesic
Almost complete convergence for the sequence of approximate solutions in linear calibration problem with α−mixing random data
Filomat, Tome 34 (2020) no. 10, p. 3251

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DOI

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.
DOI : 10.2298/FIL2010251K
Classification : 47A52, 53C38, 62L20
Keywords: Calibration, α−mixing random data, Ill-posed problem, Almost complete convergence 
Samia Khalfoune; Halima Zerouati. Almost complete convergence for the sequence of approximate solutions in linear calibration problem with α−mixing random data. Filomat, Tome 34 (2020) no. 10, p. 3251 . doi: 10.2298/FIL2010251K
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     author = {Samia Khalfoune and Halima Zerouati},
     title = {Almost complete convergence for the sequence of approximate solutions in linear calibration problem with \ensuremath{\alpha}\ensuremath{-}mixing random data},
     journal = {Filomat},
     pages = {3251 },
     year = {2020},
     volume = {34},
     number = {10},
     doi = {10.2298/FIL2010251K},
     language = {en},
     url = {http://geodesic.mathdoc.fr/articles/10.2298/FIL2010251K/}
}
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