Mean loglikelihood and higher-order approximations

Higher-order approximations to p-values can be obtained from the loglikelihood function and a reparameterization that can be viewed as a canonical parameter in an exponential family approximation to the model. This approach clarifies the connection between Skovgaard (1996) and Fraser et al. (1999a),...

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Veröffentlicht in:Biometrika 2010-03, Vol.97 (1), p.159-170
Hauptverfasser: Reid, N., Fraser, D. A. S.
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container_title Biometrika
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creator Reid, N.
Fraser, D. A. S.
description Higher-order approximations to p-values can be obtained from the loglikelihood function and a reparameterization that can be viewed as a canonical parameter in an exponential family approximation to the model. This approach clarifies the connection between Skovgaard (1996) and Fraser et al. (1999a), and shows that the Skovgaard approximation can be obtained directly using the mean loglikelihood function.
doi_str_mv 10.1093/biomet/asq001
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source Jstor Complete Legacy; Oxford University Press Journals All Titles (1996-Current); RePEc; Alma/SFX Local Collection; JSTOR Mathematics & Statistics
subjects Applications
Approximate pivot
Approximation
Biology, psychology, social sciences
Coordinate systems
Exact sciences and technology
Fraser information
General topics
Inference
Kullback–Leibler distance
Mathematical independent variables
Mathematical models
Mathematical vectors
Mathematics
p approximation
Parameter estimation
Parametric models
Probability and statistics
Scalars
Sciences and techniques of general use
Statistics
Studies
Tangent exponential model
Tangent function
Tangents
title Mean loglikelihood and higher-order approximations
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