tokens: such as the Pareto family and the generalized student T-distributions, are often associated with long-range dependence or memory processes ( Pisarenko 2010 ; Beran et al. 2013 ). The main question in this statistical study can be formulated as: Is the CME process “purely” stochastic? Or does the CME process have a long-range dependence or “memory” in such a way that an event is influenced by the occurrence of past events? To address this question, Wheatland (2003) analyzed a set of CMEs observed by LASCO during the ascending phase and part of the maximum of cycle 23 (1996–2001). The author fit the tail of the WT distribution (WT > 10 h) by a power law function (with index γ ≈ −2.36). The author also applied a Bayesian statistical analysis and concluded that the observed WT distribution corresponds to a time-dependent Poisson process. Performing a similar analysis but using LASCO observations in a restricted time range around the maximum of cycle 23 (1999–2001), Moon et al. (2003) reached the same conclusion that the WT distribution of 3187 CMEs may be represented by a nonstationary Poisson process. A power law distribution, using only 113 CMEs observed by the Solar Maximum Mission, during the minimum phase of the cycle (1984–1989), was also found by Méndez Berhondo et al. (2006) , with γ = 1.41. In this case, the authors associated the WT with a characteristic length Λ, (between two consecutive CMEs) as Λ ∝ V A WT, where V A is the Alfvén speed (this implies causality), and conclude that the WT power law distribution is consistent with their picture of characteristic length. It is clear that a power law may be used to fit the tail of the WT distribution, but as mentioned earlier, 83% of the total events have a WT under 10 h. Therefore, the tail after this WT time is not necessarily representative of the whole process (only the extreme events fall in the extended tail of the distribution). In fact, a power law distribution is heavy-tailed and may represent a scaling system, which, in turn, may have self-organized criticality or related to processes with long-range dependence or “memory” ( Pisarenko 2010 ; Beran et al. 2013 ). For instance, Takahashi et al. (2016) found a scaling relationship between CMEs and proton events. Also, Telloni et al. (2014) found that the WT distribution may be fit by a Weibull distribution (a heavy-tailed distribution), implying some degree of correlation between CMEs in opposition to a purely stochastic process. Our findings are in agreement with Jiggens Gabriel (2009 ; who found that SEP events appear to have some memory indicating that events are not completely random) and with the scaling relationship between CMEs and SEP found by Takahashi et al. (2016) . The possible correlation between consecutive CMEs is supported by the remarkable peak at short PA diff , which shows that the source region of a large number of CMEs is located within close distance (Fig. 5 ). This result confirms the finding of Moon et
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