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In this paper we introduce a saddlepoint approximation method for higher-order moments like E(S − a) m+ ,a > 0, where the random variable S in these expectations could be a single random variabl...
In this paper we study the problem of estimating the mixing coefficients between two random vari-ables. Three different mixing coefficients are studied,namely alpha-mixing, beta-mixing and phi-mixing ...
We propose a new algorithm to compute numerically the distribution function of the sum of $d$ dependent, non-negative random variables with given joint distribution.
Let X1,X2, . . . ,Xn be independent and identically distributed random variables. We present an analytic method for computing the moments of Sn = Pn i=1 Xi. The method is illustrated with a simple...
We provide a necessary and sufficient condition for the ratio of two jointly alpha-Frechet random variables to be regularly varying. This condition is based on the spectral representation of the joint...
In this work, we deal with approximations for distribution functions of non-negative random variables. More specifically, we construct continuous approximants using an acceleration technique over a w...
Let $X_1,X_2,...,X_n$ be a sequence of independent or locally dependent random variables taking values in $\mathbb{Z}_+$. In this paper, we derive sharp bounds, via a new probabilistic method, for the...
Exact lower bounds on the exponential moments of min(y,X) and X I {X < y} are provided given the first two moments of a random variable X. These bounds are useful in work on large deviations probabi...
Under an appropriate regular variation condition, the affinely normalized partial sums of a sequence of independent and identically dis- tributed random variables converges weakly to a non-Gaussian ...
This note gives the convergence rate in the central limit theorem and the random central limit theorem of some functions of the average of independent random variables.
On the limit behaviour of random sums of independent random variables。
Notions of independence for random variables。
Conditioned limit theorems for functions of the average of i.i.d. random variables。
Weak convergence of random sums of infima of independent random variables。
Convergence rates in the strong law of large numbers for sums of random variables with multidimensional indices。

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