Mean loglikelihood and higher-order approximations

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.
Format: Artikel
Sprache:eng
Schlagworte:
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
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Beschreibung
Zusammenfassung: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.
ISSN:0006-3444
1464-3510
1464-3510
0006-3444
DOI:10.1093/biomet/asq001