Near-Record Values in Discrete Random Sequences

Given a sequence (Xn) of random variables, Xn is said to be a near-record if Xn∈(Mn−1−a,Mn−1], where Mn=max{X1,…,Xn} and a>0 is a parameter. We investigate the point process η on [0,∞) of near-record values from an integer-valued, independent and identically distributed sequence, showing that it is a Bernoulli cluster process. We derive the probability generating functional of η and formulas for the expectation, variance and covariance of the counting variables η(A),A⊂[0,∞). We also derive the strong convergence and asymptotic normality of η([0,n]), as n→∞, under mild regularity conditions on the distribution of the observations. For heavy-tailed distributions, with square-summable hazard rates, we prove that η([0,n]) grows to a finite random limit and compute its probability generating function. We present examples of the application of our results to particular distributions, covering a wide range of behaviours in terms of their right tails

Numbers of near bivariate record-concomitant observations

Let be independent and identically distributed random vectors with continuous distribution. Let L(n) and X(n) denote the nth record time and the nth record value obtained from the sequence of Xs. Let Y(n) denote the concomitant of the nth record value, which relates to the sequence of Ys. We call a near bivariate nth record-concomitant observation if belongs to the open rectangle (X(n)-a,X(n))×(Y(n)-b1,Y(n)+b2), where a,b1,b2>0 and L(n)Records Concomitants of records Near bivariate record-concomitant observations Insurance claims Limit theorems Generating of records, bivariate record-concomitants