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.

Autorentext

Johann Pfanzagl completed his degree in Mathematics at the University of Vienna in 1951 as a student of Johann Radon and Edmund Hlawka. After working for several years as a Statistician at the Chamber of Commerce in Austria he became a Professor of Statistics at the University of Vienna in 1959, and at the University of Cologne in 1960. He is one of the few remaining scholars who, in the early days, contributed to the development of statistical theory using sophisticated mathematical tools.

Klappentext

Contents: Introduction.- Survey of Basic Theory.- Semiparametric Families Admitting a Sufficient Statistic.- Auxiliary Results.- References.- Notation Index.- Subject Index.

Inhalt
A Survey of basic theory.- 1. Tangent spaces and gradients.- 2. Asymptotic bounds for the concentration of estimator-sequences.- 3. Constructing estimatorsequences.- 4. Estimation in semiparametric models.- 5. Families of gradients.- 6. Estimating equations.- B Semiparametric families admitting a sufficient statistic.- 7. A special semiparametric model.- 8. Mixture models.- 9. Examples of mixture models.- L Auxiliary results.- References.- Notation index.