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dc.titleThe estimating function bootstrap
dc.contributor.authorHu, F.
dc.contributor.authorKalbfleisch, J.D.
dc.identifier.citationHu, F.,Kalbfleisch, J.D. (2000-09). The estimating function bootstrap. Canadian Journal of Statistics 28 (3) : 449-499. ScholarBank@NUS Repository.
dc.description.abstractThe 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.
dc.subjectCommon means problem
dc.subjectConscience intervals
dc.subjectEstimating equation
dc.subjectEstimating function
dc.subjectGeneralized score
dc.subjectLinear regression
dc.subjectNonlinear regression
dc.subjectSignificance tests
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.sourcetitleCanadian Journal of Statistics
Appears in Collections:Staff Publications

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