Refine
Year of publication
Institute
Has Fulltext
- no (31) (remove)
Document Type
- Article (26)
- Book (3)
- Part of a Book (1)
- Conference Proceeding (1)
Keywords
- Acceleration (1)
- Afterload (1)
- Asymptotic efficiency (1)
- Censored data (1)
- Compliance (1)
- Contractility (1)
- Esophageal Doppler monitor (1)
- Force (1)
- Kinetic energy (1)
- Product-integration (1)
Weak Representation of the Cumulative Hazard Function under Semiparametric Random Censorship Models
(2001)
Kompakte und perfekte Maße
(1983)
We study the estimation of some linear functionals which are based on an unknown lifetime distribution. The observations are assumed to be generated under the semi-parametric random censorship model (SRCM), that is, a random censorship model where the conditional expectation of the censoring indicator given the observation belongs to a parametric family. Under this setup a semi-parametric estimator of the survival function was introduced by the author. If the parametric model assumption is correct, it is known that the estimated functional which is based on this semi-parametric estimator is asymptotically at least as efficient as the corresponding one which rests on the nonparametric Kaplan–Meier estimator.
In this paper we show that the estimated functional which is based on this semi-parametric estimator is asymptotically efficient with respect to the class of all regular estimators under this semi-parametric model.
This book provides a compact introduction to the bootstrap method. In addition to classical results on point estimation and test theory, multivariate linear regression models and generalized linear models are covered in detail. Special attention is given to the use of bootstrap procedures to perform goodness-of-fit tests to validate model or distributional assumptions. In some cases, new methods are presented here for the first time.
The text is motivated by practical examples and the implementations of the corresponding algorithms are always given directly in R in a comprehensible form. Overall, R is given great importance throughout. Each chapter includes a section of exercises and, for the more mathematically inclined readers, concludes with rigorous proofs. The intended audience is graduate students who already have a prior knowledge of probability theory and mathematical statistics.