Published June 1990
by Springer .
Written in English
|The Physical Object|
|Number of Pages||112|
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. About this book This book is about estimation in situations where we believe we have enough knowledge to model some features of the data parametrically, but are unwilling to assume anything for other features. Such models have arisen in a wide variety of contexts in recent years, particularly in economics, epidemiology, and : Springer-Verlag New York. , Vol Series A, Pt. 1, pp. – Eﬃcient and Adaptive Estimation for Semiparametric Models P.J. Bickel, C.A.J. Klaassen, Y. Ritov and J.A. Wellner Springer Verlag This book is a reprint of the book that appeared with Johns Hopkins Uni- versity Press in BOOK 3: Efficient and Adaptive Estimation for Semiparametric Models with P. J. Bickel, C.A.J. Klaassen, and Y. Ritov; Published by Johns Hopkins University Press, Baltimore.
Ch. Estimation of Semiparametric Models class of GMM estimators is available, based upon the general moment condition. E(d(x)Cl(e(y,x,cr,))-v,l} =O () for any conformable functions d.) and I.) for which the moment in () is well-defined, with v,, = EC/(s)]. we therefore consider a more general semiparametric time series model than model () and propose using a nonlinear least squares (LS) estimation method to deal with the es- timation of the. Estimation in a semiparametric model for longitudinal data with unspecified dependence structure Xuming He. Search for other works by this author on: Estimation in a semiparametric model for longitudinal data with unspecified Cited by: Ch. Estimation of Semiparametric Models functions is the class of linear latent variable models, in which the dependent variable y is assumed to be generated as some transformation y = t(y*; 20, %.)) () of some unobservable variable y*, which itself has .
(). Penalized Estimating Functions and Variable Selection in Semiparametric Regression Models. Journal of the American Statistical Association: Vol. , No. , pp. Cited by: The theory of missing data applied to semiparametric models is scattered throughout the literature with no thorough comprehensive treatment of the subject. This book combines much of what is known in regard to the theory of estimation for semiparametric models with missing data in an organized and comprehensive manner.5/5(6). Semiparametric regression can be of substantial value in the solution of complex scientiﬁc problems. The real world is far too complicated for the human mind to comprehend in great detail. Semiparametric regression models reduce complex data sets to summaries that . The literature on nonparametric and semiparametric estimation is large and highly technical. This book presents the main ideas underlying a variety of nonparametric and semiparametric methods.