Article ID Journal Published Year Pages File Type
1150119 Journal of Statistical Planning and Inference 2011 15 Pages PDF
Abstract
In this paper, we derive explicit computable expressions for the asymptotic distribution of the maximum likelihood estimate of an unknown change-point in a sequence of independently and exponentially distributed random variables. First we state and prove a theorem that shows asymptotic equivalence of the change-point mle for the cases of both known and unknown parameters, respectively. Thereafter, the computational form of the asymptotic distribution of the change-point mle is derived for the case of known parameter situation only. Simulations show that the distribution for the known case applies very well to the case where the parameters are estimated. Further, it is seen from simulations that the derived unconditional mle shows better performance compared to the conditional solution of Cobb. Application of change detection methodology and the derived estimation methodology show strong support in favor the dynamic triggering hypothesis for seismic faults in Sumatra, Indonesia region.
Related Topics
Physical Sciences and Engineering Mathematics Applied Mathematics
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