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  • We prove a central limit theorem for conditionally centered random field, under condition of strict positivity of the empirical variance per observation. We use a random normalization, which fits to non-stationary situations. The theorem directly applied to Markov random fields, including the case of phase transition and lack of stationarity. A consequence is the asymptotic normality of a statistics for testing a composite hypotheses on a parameter of Markov fields in complete generality.
  • We prove a central limit theorem for conditionally centered random field, under condition of strict positivity of the empirical variance per observation. We use a random normalization, which fits to non-stationary situations. The theorem directly applied to Markov random fields, including the case of phase transition and lack of stationarity. A consequence is the asymptotic normality of a statistics for testing a composite hypotheses on a parameter of Markov fields in complete generality. (en)
Title
  • A central limit theorem for conditionally centred random fields with an application to testing statistical hypotheses.
  • A central limit theorem for conditionally centred random fields with an application to testing statistical hypotheses. (en)
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  • A central limit theorem for conditionally centred random fields with an application to testing statistical hypotheses.
  • A central limit theorem for conditionally centred random fields with an application to testing statistical hypotheses. (en)
skos:notation
  • RIV/67985556:_____/02:16030055!RIV/2004/AV0/A16004/N
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  • 209;223
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  • P(GA201/99/0269), Z(AV0Z1075907)
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  • 636929
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  • RIV/67985556:_____/02:16030055
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  • central limit theorem; conditionally centred random fields; composite hypotheses (en)
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  • [F5666BFDA81B]
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  • Balatonlelle [HU]
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  • Budapest
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  • Limit Theorems in Probability and Statistics.
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  • Janžura, Martin
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number of pages
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  • János Bolyai Mathematical Society
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