Abstract
Let $\{B_k\}_{k=1}^\infty , \{X_k\}_{k=1}^\infty $ all be independent random variables. Assume that $\{B_k\}_{k=1}^\infty $ are $\{0,1\}$-valued Bernoulli random variables satisfying $B_k\stackrel{\text {dist}} {=}\text{Ber} (p_k)$, with $\sum _{k=1}^\infty p_k=\infty $, and assume that $\{X_k\}_{k=1}^\infty $ satisfy $X_k>0$ and $\mu _k\equiv EX_k<\infty $. Let $M_n=\sum _{k=1}^np_k\mu _k$, assume that $M_n\to \infty $ and define the normalized sum of independent random variables $W_n=\frac 1{M_n}\sum _{k=1}^nB_kX_k$. We give a general condition under which $W_n\stackrel{\text {dist}} {\to }c$, for some $c\in [0,1]$, and a general condition under which $W_n$ converges weakly to a distribution from a family of distributions that includes the generalized Dickman distributions GD$(\theta ),\theta >0$. In particular, we obtain the following result, which reveals a strange domain of attraction to generalized Dickman distributions. Assume that $\lim _{k\to \infty }\frac{X_k} {\mu _k}\stackrel{\text {dist}} {=}1$. Let $J_\mu ,J_p$ be nonnegative integers, let $c_\mu ,c_p>0$ and let $ \mu _n\sim c_\mu n^{a_0}\prod _{j=1}^{J_\mu }(\log ^{(j)}n)^{a_j},\ p_n\sim c_p\big ({n^{b_0}\prod _{j=1}^{J_p}(\log ^{(j)}n)^{b_j}}\big )^{-1}, \ b_{J_p}\neq 0, $ where $\log ^{(j)}$ denotes the $j$th iterate of the logarithm. If \[ i.\ J_p\le J_\mu ;\\ ii.\ b_j=1, \ 0\le j\le J_p;\\ iii.\ a_j=0, \ 0\le j\le J_p-1,\ \text{and} \ \ a_{J_p}>0, \] then $\lim _{n\to \infty }W_n\stackrel{\text {dist}} {=}\frac 1{\theta }\text{GD} (\theta ),\ \text{where} \ \theta =\frac{c_p} {a_{J_p}}. $ Otherwise, $\lim _{n\to \infty }W_n\stackrel{\text {dist}} {=}\delta _c$, where $c\in \{0,1\}$ depends on the above parameters. We also give an application to the statistics of the number of inversions in certain random shuffling schemes.
Highlights
Introduction and statement of resultsThe Dickman function ρ1 is the unique function, continuous on (0, ∞), and satisfying the differential-delay equation ρ1(x) = 0, x ≤ 0; ρ1(x) = 1, x ∈ (0, 1]; xρ1(x) + ρ1(x − 1) = 0, x > 1.This function has an interesting role in number theory and probability, which we describe briefly in the final section of the paper
One can show that the Laplace transform of ρ1 is given by ∞ ρ1(x)e−λxdx = exp(γ + 1 e−λx−1 dx), where γ is Euler’s
We denote its density by p1(x) = e−γ ρ1(x), and we denote by D1 a random variable distributed according to the Dickman distribution
Summary
(When X ≡ 1, we revert to the previous notation for generalized Dickman distributions.) Differentiating the Laplace transform at λ = 0 shows that EDθ(X ) = θEX. We see that generalized Dickman distributions sometimes arise as limits of normalized sums from a sequence {Vk}∞ k=1 of independent random variables which are non-negative and satisfy. Xk μ d=ist 1, because if a sequence {Yk}∞ k=1 of random variables satisfies Yk d→ist Y , k and E|Yk| < ∞, E|Yk| → E|Y | is equivalent to uniform integrability. We provide a rather probabilistic proof that the distribution whose Laplace transform is given by exp(θ 01 e−λxx−1 dx) possesses a density pθ of the form pθ = cθρθ, where ρθ satisfies (1.1).
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