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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Estimation in Semiparametric Models: Some Recent Developments

Estimation in Semiparametric Models: Some Recent Developments

Pfanzagl, Johann

Paperback

Series: Lecture Notes in Statistics, Book 63

General MathematicsProbability & Statistics

ISBN10: 0387972382
ISBN13: 9780387972381
Publisher: Springer Pg
Published: Apr 6 1990
Pages: 112
Weight: 0.45
Height: 0.25 Width: 6.69 Depth: 9.61
Language: English
Assume one has to estimate the mean J x P( dx) (or the median of P, or any other functional t;;(P)) on the basis ofi.i.d. observations from P. Ifnothing is known about P, then the sample mean is certainly the best estimator one can think of. If P is known to be the member of a certain parametric family, say {Po: {) E e}, one can usually do better by estimating {) first, say by {)(n)(. .), and using J XPo(n)(;r.) (dx) as an estimate for J xPo(dx). There is an intermediate range, where we know something about the unknown probability measure P, but less than parametric theory takes for granted. Practical problems have always led statisticians to invent estimators for such intermediate models, but it usually remained open whether these estimators are nearly optimal or not. There was one exception: The case of adaptivity, where a nonparametric estimate exists which is asymptotically optimal for any parametric submodel. The standard (and for a long time only) example of such a fortunate situation was the estimation of the center of symmetry for a distribution of unknown shape.

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