Hypotheses optimal testing via large deviations techniques
Küçük Resim Yok
Tarih
2009
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Maltepe Üniversitesi
Erişim Hakkı
CC0 1.0 Universal
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
Özet
The problem of many hypotheses testing for a model consisting of L > 2 hypotheses on distribution of a Markov chain is studied. We apply large deviations techniques (LDT) and the method of types to the empirical distributions of finite states of Markov chain. It is proved that this method of investigation in solving the problem of logarithmically asymptotically optimal (LAO) hypotheses testing is easier than the procedure that was introduced by Haroutunian. The matrix of exponents E = {El|m}, m, l = 1, L of error probabilities of the LAO test El|m(?) = lim N?? ? 1 N log ?l|m(?N ), where ? N l|m(?N ) for l 6= m is the probability to accept the hypothesis l, when the hypothesis m is true, is determined.
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Hypotheses optimal testing via large deviations techniques
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Künye
Navaei, L. (2009). Hypotheses optimal testing via large deviations techniques. Maltepe Üniversitesi. s. 233.
