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Title: The estimating function bootstrap
Authors: Hu, F. 
Kalbfleisch, J.D.
Keywords: Bootstrap
Common means problem
Conscience intervals
Estimating equation
Estimating function
Generalized score
Linear regression
Nonlinear regression
Significance tests
Issue Date: Sep-2000
Citation: Hu, F.,Kalbfleisch, J.D. (2000-09). The estimating function bootstrap. Canadian Journal of Statistics 28 (3) : 449-499. ScholarBank@NUS Repository.
Abstract: The authors propose a bootstrap procedure which estimates the distribution of estimating function by resampling its terms using bootstrap techniques. Studentized versions of this so-called estimating function (EF) bootstrap yield methods which are invariant under reparametrizations. This approach often has substantial advantage, both in computation and accuracy, over more traditional bootstrap methods and it applies to a wide class of practical problems where the data are independent but not necessarily identically distributed. The methods allow for simultaneous estimation of vector parameters and their components. The authors use simulations to compare the EF bootstrap with competing methods in several examples including the common means problem and nonlinear regression. They also prove symptotic results showing that the studentized EF bootstrap yields higher order approximations for the whole vector parameter in a wide class of problems.
Source Title: Canadian Journal of Statistics
ISSN: 03195724
Appears in Collections:Staff Publications

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