Records for Some Stationary Dependent Sequences

07/01/2018 ∙ by Michael Falk, et al. ∙ 0

For a zero-mean, unit-variance second-order stationary univariate Gaussian process we derive the probability that a record at the time n, say X_n, takes place and derive its distribution function. We study the joint distribution of the arrival time process of records and the distribution of the increments between the first and second record, and the third and second record and we compute the expected number of records. We also consider two consecutive and non-consecutive records, one at time j and one at time n and we derive the probability that the joint records (X_j,X_n) occur as well as their distribution function. The probability that the records X_n and (X_j,X_n) take place and the arrival time of the n-th record, are independent of the marginal distribution function, provided that it is continuous. These results actually hold for a second-order stationary process with Gaussian copulas. We extend some of these results to the case of a multivariate Gaussian process. Finally, for a strictly stationary process satisfying some mild conditions on the tail behavior of the common marginal distribution function F and the long-range dependence of the extremes of the process, we derive the asymptotic probability that the record X_n occurs and derive its distribution function.



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