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Subject Item
dbr:Formation_matrix
rdfs:label
Formation matrix
rdfs:comment
In statistics and information theory, the expected formation matrix of a likelihood function is the matrix inverse of the Fisher information matrix of , while the observed formation matrix of is the inverse of the observed information matrix of . These matrices appear naturally in the asymptotic expansion of the distribution of many statistics related to the likelihood ratio.
dcterms:subject
dbc:Information_theory dbc:Estimation_theory
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6843217
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784699837
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dbc:Estimation_theory dbr:Statistics dbc:Information_theory dbr:Observed_information_matrix dbr:Asymptotic_expansion dbr:Information_theory dbr:Fisher_information dbr:Likelihood_function dbr:Fisher_information_matrix dbr:Likelihood-ratio_test dbr:Information_Geometry dbr:Einstein_notation dbr:Covariance_and_contravariance_of_vectors dbr:Peter_McCullagh dbr:Ole_E._Barndorff-Nielsen dbr:Differential_geometry dbr:Shannon_entropy
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wikidata:Q5470034 n13:4k12o freebase:m.0gs52v
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dbo:abstract
In statistics and information theory, the expected formation matrix of a likelihood function is the matrix inverse of the Fisher information matrix of , while the observed formation matrix of is the inverse of the observed information matrix of . Currently, no notation for dealing with formation matrices is widely used, but in books and articles by Ole E. Barndorff-Nielsen and Peter McCullagh, the symbol is used to denote the element of the i-th line and j-th column of the observed formation matrix. The geometric interpretation of the Fisher information matrix (metric) leads to a notation of following the notation of the (contravariant) metric tensor in differential geometry. The Fisher information metric is denoted by so that using Einstein notation we have . These matrices appear naturally in the asymptotic expansion of the distribution of many statistics related to the likelihood ratio.
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wikipedia-en:Formation_matrix?oldid=784699837&ns=0
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1911
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wikipedia-en:Formation_matrix