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Estimation in Semiparametric Models: Some Recent Developments (Lecture Notes in Statistics) by Johann Pfanzagl (1990-04-06)

Johann Pfanzagl

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Description: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.We have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Estimation in Semiparametric Models: Some Recent Developments (Lecture Notes in Statistics) by Johann Pfanzagl (1990-04-06). To get started finding Estimation in Semiparametric Models: Some Recent Developments (Lecture Notes in Statistics) by Johann Pfanzagl (1990-04-06), you are right to find our website which has a comprehensive collection of manuals listed.
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Description: 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.We have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Estimation in Semiparametric Models: Some Recent Developments (Lecture Notes in Statistics) by Johann Pfanzagl (1990-04-06). To get started finding Estimation in Semiparametric Models: Some Recent Developments (Lecture Notes in Statistics) by Johann Pfanzagl (1990-04-06), you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.

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Estimation in Semiparametric Models: Some Recent Developments (Lecture Notes in Statistics) by Johann Pfanzagl (1990-04-06)

Estimation in Semiparametric Models: Some Recent Developments (Lecture Notes in Statistics) by Johann Pfanzagl (1990-04-06)

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