9780387972381 - Estimation in Semiparametric Models: Some Recent Developments (Lecture Notes in Statistics): 63 di Pfanzagl, Johann (11 risultati)

Trade Paperback. Condizione: VG-. used trade paperback edition. lightly shelfworn, corners somewhat bumped. spine may be a bit sunned. light scuffing to covers, bottom edge of text block may be lightly discolored from shelfwear. pages and binding are clean, straight and tight. there are no marks to the text or other serious flaws.

Condizione: New. In.

PF. Condizione: New.

Condizione: New. pp. iii + 112 Softcover Reprint of the Original 1st Edition.

Paperback. Condizione: Brand New. 115 pages. 9.53x6.69x0.28 inches. In Stock.

Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 120 pp. Englisch.

Condizione: New. Print on Demand pp. iii + 112.

Condizione: New. PRINT ON DEMAND pp. iii + 112.

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 t.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 120 pp. Englisch.