A central limit theorem for conditionally centred random fields with an application to Markov fields
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We prove a central limit theorem for conditionally centred random fields, under a moment condition and strict positivity of the empirical variance per observation. We use a random normalization, which fits non-stationary situations. The theorem applies directly to Markov random fields, including the cases of phase transition and lack of stationarity. One consequence is the asymptotic normality of the maximum pseudo-likelihood estimator for Markov fields in complete generality.
A central limit theorem for conditionally centred random fields with an application to Markov fields
1998 ◽
Vol 35
(3)
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pp. 608-621
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2021 ◽
Vol 179
(3-4)
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pp. 1145-1181
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1987 ◽
Vol 26
(3)
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pp. 272-287
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1998 ◽
Vol 110
(3)
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pp. 397-426
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1994 ◽
Vol 10
(2)
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pp. 223-224
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