Asymptotics and the theory of inference
N. Reid
Source: Ann. Statist. Volume 31, Number 6 (2003), 1695-2095.
Abstract
Asymptotic analysis has always been very useful for deriving distributions in statistics in cases where the exact distribution is unavailable. More importantly, asymptotic analysis can also provide insight into the inference process itself, suggesting what information is available and how this information may be extracted. The development of likelihood inference over the past twenty-some years provides an illustration of the interplay between techniques of approximation and statistical theory.
Primary Subjects: 62-02
Secondary Subjects: 62E20, 62F05
Keywords: Ancillarity; approximation; Bayesian inference; conditioning; Laplace approximation; likelihood; matching priors; $p$*; $p$-values; $r$*; saddlepoint approximation; tail area; tangent exponential model
Full-text: Open access
Links and Identifiers
Permanent link to this document: http://projecteuclid.org/euclid.aos/1074290325
Digital Object Identifier: doi:10.1214/aos/1074290325
Mathematical Reviews number (MathSciNet):
MR2036388
Zentralblatt MATH identifier:
02067665
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Mathematical Reviews (MathSciNet):
MR1933009
The Annals of Statistics
