A perturbative approach is used to derive approximations of arbitrary order to estimate high percentiles of sums of positive independent random variables that exhibit heavy tails. Closed-form expressions for the successive approximations are obtained both when the number of terms in the sum is deterministic and when it is random. The zeroth order approximation is the percentile of the maximum term in the sum. Higher orders in the perturbative series involve the right-truncated moments of the individual random variables that appear in the sum. These censored moments are always finite. As a result, and in contrast to previous approximations proposed in the literature, the perturbative series has the same form regardless of whether these random variables have a finite mean or not. The accuracy of the approximations is illustrated for a variety of distributions and a wide range of parameters. The quality of the estimate improves as more terms are included in the perturbative series, specially for higher percentiles and heavier tails.
Abstract The present paper investigates, for the general Andersen model, the asymptotic behaviour of the probability of ruin function when the initial risk reserve tends to infinity. Whereas the exponential (Cramer) case is well understood, in the past, less attention has been paid to a systematic study of a model taking big claim sizes into account. We give a thorough treatment of the latter and also review previously known but mostly scattered results to show how they all follow from essentially one mathematical model.  Preface.- Introduction.- Heavy- and long-tailed distributions.- Subexponential distributions.- Densities and local probabilities.- Maximum of random walks.- References.- Index  Let X i (i=1,2, …) be a sequence of subexponential positive independent and identically distributed random variables. In this paper, we offer two alternative approaches to obtain higher-order expansions of the tail of and subsequently for ruin probabilities in renewal risk models with claim sizes X i . In particular, these emphasize the importance of the term for the accuracy of the resulting asymptotic expansion of . Furthermore, we present a more rigorous approach to the often suggested technique of using approximations with shifted arguments. The cases of a Pareto type, Weibull and Lognormal distribution for X i are discussed in more detail and numerical investigations of the increase in accuracy by including higher-order terms in the approximation of ruin probabilities for finite realistic ranges of s are given.  Let @math be independent random positive variables and let @math , @math Let us denote [ P _1 + + _n < t = G_n (t). ]Theorem. [ t 1 - G_n (t) 1 - G(t) = n, n = 1,2,3, , ]if and only if [ t 1 - G_2 (t) 1 - G(t) = 2. ] This theorem is useful in some investigations of age-dependent branching processes. Let G = [Sigma][infinity]n=0pnF*n denote the probability measure subordinate to F with subordinator Pn . We investigate the asymptotic behaviour of (1 - G(x))-([Sigma] npn)(1 - F(x)) as x --> [infinity] if 1 - F is regularly varying with index [varrho], 0 We derive an asymptotic expansion for the distribution of a compound sum of independent random variables, all having the same rapidly varying subexponential distribution. The examples of a Poisson and geometric number of summands serve as an illustration of the main result. Complete calculations are done for a Weibull distribution, with which we derive, as examples and without any difficulties, seven-term expansions. In this paper we construct asymptotic expansions for the tail area of a compound sum, when the distribution of the summands belongs to a class of rapidly varying subexponential distributions. To be more precise, let X?, i > 1, be a sequence of independent random variables, all having the same distribution, F. For any positive integer n the distribution of the partial sums Sn = X + + Xn is the n-fold convolution F*n. We set So = 0 and therefore F*? is defined as the distribution of the point mass at the origin. Let N be a nonnegative integer-valued random variable, independent of the X s. We consider the distribution G of the compound sum Sn, that is E F*N, and we seek an asymptotic expansion for its tail area G = 1 ? G. First-order asymptotic results for G have been obtained by (1979), Cline (1987), and Embrechts (1985). A second-order formula may be found in Griibel (1987) and Omey and Willekens (1987). Compound sums or subordinated distributions arise as distributions of interest in several stochastic models. In insurance risk theory, it models the total claim amount. For a discussion of issues related to random sums and insurance risk, we refer the reader to Embrechts et   In this paper we derive precise tail-area approximations for the sum of an arbitrary finite number of independent heavy-tailed random variables. In order to achieve second-order asymptotics, a mild regularity condition is imposed on the class of distribution functions with regularly varying tails. @PARASPLIT Higher-order asymptotics are also obtained when considering a semiparametric subclass of distribution functions with regularly varying tails. These semiparametric subclasses are shown to be closed under convolutions and a convolution algebra is constructed to evaluate the parameters of a convolution from the parameters of the constituent distributions in the convolution. A Maple code is presented which does this task. where (an)n ENo is some sequence of nonnegative numbers, (Sn),nENo is the sequence of partial sums, S0 = 0, Sn = XflXk, of another sequence (Xk)kEN of i.i.d. random variables, and A c R is a fixed Borel set such as [0,1] or [0, oo). Examples of such convolution series are subordinated distributions (f=0Oan = 1) which arise as distributions of random sums, and harmonic and ordinary renewal measures (a0 = 0, an = 1 n for all n C N in the first, an = 1 for all n C NO in the second case). These examples are in turn essential for the analysis of the large time behaviour of diverse applied models such as branching and queueing processes, they are also of interest in connection with representation theorems such as the Levy representation of infinitely divisible distributions. A traditional approach to such problems is via regular variation: If the underlying random variables are nonnegative we can use Laplace transforms and the related Abelian and Tauberian theorems [see, e.g., Stam (1973) in the context of subordination and Feller (1971, XIV.3) in connection with renewal theory; Embrechts, Maejima, and Omey (1984) is a recent treatment of generalized renewal measures along these lines]. The approach of the present paper is based on the Wiener-Levy-Gel'fand theorem and has occasionally been called the Banach algebra method. In Gruibel (1983) we gave a new variant of this method for the special case of lattice distributions, showing that by using the appropriate Banach algebras of sequences, arbitrarily fine expansions are possible under certain assumptions on the higher-order differences of (P(X1 = n))fnEN. Here we give a corresponding treatment of nonlattice distributions. We restrict ourselves to an analogue of first-order differences and obtain a number of theorems which perhaps are described best as next-term results. To explain this let us consider a special case in more detail.  Abstract : The Memorandum is an extension of certain analytic results obtained in AD-646 366, A Comparison of Average Likelihood and Maximum Likelihood Ratio Tests for Detecting Radar Targets of Unknown Doppler Frequency, L. E. Brennan, I. S. Reed, and W. Sollfrey, December 1966. It is shown that some asymptotic results derived in AD-646 366 are valid over a much broader range than originally indicated, providing a useful analytic tool for investigating more complicated radar detection problems involving signals of unknown frequency.