Asymptotics in Statistics: Some Basic Concepts
Springer-Verlag, 1990 - Mathematics - 180 pages
This work presents a coherent introduction to asymptotic statistics. This Second Edition includes a new chapter on Gaussian and Poisson experiments because of their growing role in the field, especially in nonparametrics and semi-parametrics. Many chapters have been entirely rewritten and the nonparametrics material has been amplified.
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Contiguity Hellinger Transforms
Limit Laws for Likelihood Ratios Obtained from
3 other sections not shown
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according already apply approximation argument Assume assumptions asymptotic bounded called centered Chapter close compact consider construction continuous convergence defined definition density depend described distance distribution equal equivalent estimates example exist expectation experiments extend fact finite fixed follows function Gaussian given gives Hellinger hold implies independent inequality instance integral involve joint LAMN LAN conditions Le Cam Lebesgue Lemma likelihood ratios limit linear look loss matrix means normal Note observations obtained pair parameter particular Po,n positive possible posterior prior probability measures problem proof properties Proposition prove quadratic random relation Remark replaced respect restricted result risk satisfied sense sequence shift situation space Statistics subset sufficient surely taken tends to zero term theorem theory transforms true values variables vector