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Unified Asymptotic Results for Maximum Spacing and Generalized Spacing Methods for Continuous Models

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Document pages: 26 pages

Abstract: Asymptotic results are obtained using an approachbased on limit theorem results obtained for α-mixingsequences for the class of general spacings (GSP) methods which include themaximum spacings (MSP) method. The MSP method has been shown to be very usefulfor estimating parameters for univariate continuous models with a shift at theorigin which are often encountered in loss models of actuarial science andextreme models. The MSP estimators have also been shown to be as efficient asmaximum likelihood estimators in general and can be used as an alternativemethod when ML method might have numerical difficulties for some parametricmodels. Asymptotic properties are presented in a unified way. Robustness results for estimation and parametertesting results which facilitate the applications of the GSP methods are alsoincluded and related to quasi-likelihood results.

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