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dc.contributor.authorFan, Rocky Yuk-Keung.en_US
dc.date.accessioned2014-10-21T12:36:03Z
dc.date.available1994
dc.date.issued1994en_US
dc.identifier.otherAAINN98842en_US
dc.identifier.urihttp://hdl.handle.net/10222/55438
dc.descriptionIn this thesis we present a finite sample approximation for the marginal densities of a multivariate M-estimator. The result is particularly useful in robust statistics where an estimator usually is defined implicitly and does not have a closed form, and for small sample problems where the asymptotic results may not be reliable.en_US
dc.descriptionPrecisely, let Y$\sb1,\... ,Y\sb{n}$ be independent m-dimensional random observations such that each observation has a density function which is parameterized by a p-dimensional parameter $\eta$. Let $\\eta$ be an M-estimator of $\eta$, the solution of the systemen_US
dc.description$${1\over n}\Sigma\sbsp{l=1}{n} \Psi\sb{jl}(Y\sb{l},\\eta) = 0,\ j = 1,\...,p.$$Our primary objective is to derive an approximation for the marginal densities of a component in $\\eta$ under $\eta = \eta\sb0.$ The result is then extended to a real-valued function $\rho(\\eta),\ \rho : Re\sp\rho \to \Re$, and finally to a real-valued vector $\rho(\\eta) = \{\rho\sb1(\\eta),\...,\rho\kappa(\\eta)\},\ \rho : Re\sp\rho \to Re\sp{k},\ k \le p$.en_US
dc.descriptionWe begin with an overview of the general problem and some background information. Then we derive the main results and discuss the relationship among our approach and some existing techniques for the problem. In addition, we implement the approximations for several location-scale and multiple regression examples. Finally, we discuss the limitation and some potential applications of our results.en_US
dc.descriptionThesis (Ph.D.)--Dalhousie University (Canada), 1994.en_US
dc.languageengen_US
dc.publisherDalhousie Universityen_US
dc.publisheren_US
dc.subjectStatistics.en_US
dc.titleApproximations for marginal densities of M-estimators.en_US
dc.typetexten_US
dc.contributor.degreePh.D.en_US
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