Estimation in Semiparametric Models - Some Recent Developments (Paperback, Softcover reprint of the original 1st ed. 1990)

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.

General

Imprint:	Springer-Verlag New York
Country of origin:	United States
Series:	Lecture Notes in Statistics, 63
Release date:	April 1990
First published:	1990
Authors:	Johann Pfanzagl
Dimensions:	242 x 170 x 6mm (L x W x T)
Format:	Paperback
Pages:	112
Edition:	Softcover reprint of the original 1st ed. 1990
ISBN-13:	978-0-387-97238-1
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Promotions
LSN:	0-387-97238-2
Barcode:	9780387972381

Is the information for this product incomplete, wrong or inappropriate? Let us know about it.

Does this product have an incorrect or missing image? Send us a new image.

Is this product missing categories? Add more categories.

Review This Product

No reviews yet - be the first to create one!