AbstractLet {X,Xi;i⩾1} be a sequence of independent and identically distributed positive random variables, which is in the domain of attraction of the normal law, and tn be a positive, integer random variable. Denote Sn=∑i=1nXi, Vn2=∑i=1n(Xi−X¯)2, where X¯ denotes the sample mean. Then we show that the self-normalized random product of the partial sums, (∏k=1tnSkkμ)μVtn, is still asymptotically lognormal under a suitable condition about tn
We determine exactly when a certain randomly weighted self{normalized sum converges in distribution,...
AbstractWe show that most random walks in the domain of attraction of a symmetric stable law have a ...
AbstractLet N(n,i) = (k,…,kn,n−ik)ci/i, i = O.…,[n/k]. We prove that the random variable Xn such tha...
AbstractLet {X,Xi;i⩾1} be a sequence of independent and identically distributed positive random vari...
AbstractLet X,X1,X2,… be a sequence of independent and identically distributed positive random varia...
AbstractLet X,X1,X2,… be a sequence of nondegenerate i.i.d. random variables with zero means. Set Sn...
AbstractLet {X,Xn;n≥1} be a sequence of independent and identically distributed (i.i.d.) random vari...
Let X, X1, X2,... be i.i.d. nondegenerate random variables, Sn = ∑j=1n Xj and Vn2 = ∑j=1n. We invest...
We give conditions under which the self-normalized productof independent and identically distributed...
AbstractLet X,X1,X2,… be i.i.d. nondegenerate random variables with zero means, Sn=∑j=1nXj and Vn2=∑...
AbstractLet {Xi,i≥1} be a sequence of i.i.d. random variables which is in the domain of attraction o...
AbstractLet X1,X2,… be i.i.d. random variables with distribution μ and with mean zero, whenever the ...
If Xi are i.i.d. and have zero mean and arbitrary finite variance the limiting probability distribut...
AbstractThe original Erdős—Rényi theorem states that max0⩽k⩽n∑k+[clogn]i=k+1Xi/[clogn]→α(c),c>0, alm...
Let X,X1,X2,… be a sequence of independent and identically distributed random variables in the domai...
We determine exactly when a certain randomly weighted self{normalized sum converges in distribution,...
AbstractWe show that most random walks in the domain of attraction of a symmetric stable law have a ...
AbstractLet N(n,i) = (k,…,kn,n−ik)ci/i, i = O.…,[n/k]. We prove that the random variable Xn such tha...
AbstractLet {X,Xi;i⩾1} be a sequence of independent and identically distributed positive random vari...
AbstractLet X,X1,X2,… be a sequence of independent and identically distributed positive random varia...
AbstractLet X,X1,X2,… be a sequence of nondegenerate i.i.d. random variables with zero means. Set Sn...
AbstractLet {X,Xn;n≥1} be a sequence of independent and identically distributed (i.i.d.) random vari...
Let X, X1, X2,... be i.i.d. nondegenerate random variables, Sn = ∑j=1n Xj and Vn2 = ∑j=1n. We invest...
We give conditions under which the self-normalized productof independent and identically distributed...
AbstractLet X,X1,X2,… be i.i.d. nondegenerate random variables with zero means, Sn=∑j=1nXj and Vn2=∑...
AbstractLet {Xi,i≥1} be a sequence of i.i.d. random variables which is in the domain of attraction o...
AbstractLet X1,X2,… be i.i.d. random variables with distribution μ and with mean zero, whenever the ...
If Xi are i.i.d. and have zero mean and arbitrary finite variance the limiting probability distribut...
AbstractThe original Erdős—Rényi theorem states that max0⩽k⩽n∑k+[clogn]i=k+1Xi/[clogn]→α(c),c>0, alm...
Let X,X1,X2,… be a sequence of independent and identically distributed random variables in the domai...
We determine exactly when a certain randomly weighted self{normalized sum converges in distribution,...
AbstractWe show that most random walks in the domain of attraction of a symmetric stable law have a ...
AbstractLet N(n,i) = (k,…,kn,n−ik)ci/i, i = O.…,[n/k]. We prove that the random variable Xn such tha...