Explaining the saddlepoint approximation

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dc.contributor.author Goutis, Constantinos
dc.contributor.author Casella, George
dc.contributor.editor Universidad Carlos III de Madrid. Departamento de Estadística
dc.date.accessioned 2011-04-13T18:20:51Z
dc.date.available 2011-04-13T18:20:51Z
dc.date.issued 1995-12
dc.identifier.uri http://hdl.handle.net/10016/10734
dc.description.abstract Saddlepoint approximations are powerful tools for obtaining accurate expressions for densities and distribution functions. \Ve give an elementary motivation and explanation of saddlepoint approximation techniques, stressing the connection with the familiar Taylor series expansions and the Laplace approximation of integrals. Saddlepoint methods are applied to the convolution of simple densities and, using the Fourier inversion formula, the saddlepoint approximation to the density of a random variable is derived. \Ve then apply the method to densities of sample means of iid random variables, and also demonstrate the technique for approximating the density of a maximum likelihood estimator in exponential families.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.relation.ispartofseries UC3M Working papers. Statistics and Econometrics
dc.relation.ispartofseries 95-63
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Laplace method
dc.subject.other Maximum likelihood estimators
dc.subject.other Moment generating functions
dc.subject.other Taylor series
dc.title Explaining the saddlepoint approximation
dc.type workingPaper
dc.subject.eciencia Estadística
dc.rights.accessRights openAccess
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