Asymptotic results are obtained using an approach
based on limit theorem results obtained for α-mixing
sequences for the class of general spacings (GSP) methods which include the
maximum spacings (MSP) method. The MSP method has been shown to be very useful
for estimating parameters for univariate continuous models with a shift at the
origin which are often encountered in loss models of actuarial science and
extreme models. The MSP estimators have also been shown to be as efficient as
maximum likelihood estimators in general and can be used as an alternative
method when ML method might have numerical difficulties for some parametric
models. Asymptotic properties are presented in a unified way. Robustness results for estimation and parameter
testing results which facilitate the applications of the GSP methods are also
included and related to quasi-likelihood results.
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