Analysis of Data from a Series of Events by a Geometric Process Model

Geometric process was first introduced by Lam[10,11]. A stochastic process {Xi, i = 1, 2, · · ·} is called a geometric process (GP) if, for some a > 0, {ai-1Xi, i = 1, 2, · · ·} forms a renewal process. In this paper, the GP is used to analyze the data from a series of events. A nonparametric method is introduced for the estimation of the three parameters in the GP. The limiting distributions of the three estimators are studied. Through the analysis of some real data sets, the GP model is compared with other three homogeneous and nonhomogeneous Poisson models. It seems that on average the GP model is the best model among these four models in analyzing the data from a series of events.

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