Titlebook: Estimation in Semiparametric Models; Some Recent Developm Johann Pfanzagl Book 1990 Springer-Verlag Berlin Heidelberg 1990 DEX.boundary ele

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Regenerative Cryogenic Refrigerators,s. In many cases, such e.s. are already available, and the role of the “l(fā)ocal theory” is confined to establish their as. optimality. This is, in particular, the case if the tangent space is full, i.e. .(., .) = .(.). Then there exists one gradient only, hence any as. linear e.s. is necessarily as. e

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Introductions known about ., then the sample mean is certainly the best estimator one can think of. If . is known to be the member of a certain parametric family, say {.}: ? ∈ Θ, one can usually do better by estimating ? first, say by ?.(.), and using ∫ . . .(.) (.) as an estimate for ∫ .(.). There is an “inter

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Tangent spaces and gradients a functional К. → ?, based on an i.i.d. sample (.,..., .), i.e. a realization from ., for some . ∈ .. The restriction to 1-dimensional functional makes the following presentation more transparent. It is justified by the fact that the problem of estimating an .-dimensional functional simply is the p

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