Central limit theorem for associated random variables

In this paper, we investigate an functional central limit theorem for a nonstatioaryd-parameter array of associated random variables applying the criterion of the tightness condition in Bickel and Wichura[1971]. Our results imply an extension to the nonstatioary case of invariance principle of Burton and Kim(1988) and analogous results for thed-dimensional associated random measure. These results are also applied to show a new functional central limit theorem for Poisson cluster random variables.     

Central limit theorem for finitely-dependent random variables

For sequences of finitely-dependent random variables, under rather general hypotheses we establish estimates of integrals of the form where F_n(x) is the distribution function of the normalized sum of random variables; (x) is the standard normal distribution function. In the proof we use relations obtained by the method of C. Stein. The results are applicable, in particular, to m-dependent random variables and fields.Translated from Teoriya Sluchainykh Protsessov, No. 16, pp. 93–97, 1988.     

The laws of large numbers for Pareto-type random variables under sub-linear expectation

In this paper, some laws of large numbers are established for random variables that satisfy the Pareto distribution, so that the relevant conclusions in the traditional probability space are extended to the sub-linear expectation space. Based on the Pareto distribution, we obtain the weak law of large numbers and strong law of large numbers of the weighted sum of some independent random variable sequences.     

Central limit theorem for Banach space valued fuzzy random variables

In this paper we prove a central limit theorem for Borel measurable nonseparably valued random elements in the case of Banach space valued fuzzy random variables.

     

A central limit theorem for m-dependent random variables under sublinear expectations

In this paper, we prove a central limit theorem for m-dependent random variables under sublinear expectations. This theorem can be regarded as a generalization of Peng’s central limit theorem.     

Central limit theorem for weakly dependent variables

Mathematical Notes -     

Complete moment convergence for ND random variables under the sub-linear expectations

In this article, we establish a general result on complete moment convergence for arrays of rowwise negatively dependent(ND) random variables under the sub-linear expectations. As applications, we can obtain a series of results on complete moment convergence for ND random variables under the sub-linear expectations.     

Central limit theorem for stationary random fields

Institute of Mathematics and Cybernetics, Academy of Sciences of the Lithuanian SSR. Translated from Litovskii Matematicheskii Sbornik (Lietuvos Matematikos Rinkinys), Vol. 28, No. 4, pp. 758–769, October–December, 1988.     

A central limit theorem for fuzzy random variables

Fuzzy random variables have been proposed to treat situations in which both random behavior and fuzzy perception must be considered. A definition of independence is given for fuzzy random variables, as well as a notion of fuzzy Gaussian random variables. It is shown that a sum or mean of independent fuzzy random variables converges in the limit to a fuzzy Gaussian random variable, thus providing a fuzzy analogue of the central limit theorem of classical probability theory.     

A local limit theorem for independent random variables

A local limit theorem is given for independent noninteger random variables under a condition which is more general than one previously given, and which reduces, in the case of identically distributed random variables, to a well-known result.     

A central limit theorem for random sums of random variables

Anscombe (1952) (also see Chung (1974)) has developed a central limit theoremof random sums of independent and identically distributed random variables. Applicability of this theorem in practice, however, is limited since the normalization requires random factors. In this paper we establish sufficient conditions under which the central limit theorem holds when such random factors are replaced by the underlying asymptotic mean and standard ddeviation. An application of this result in the context of shock models is also given.     

Central limit theorems for stochastic processes under random entropy conditions

Summary Necessary and sufficient conditions are found for the weak convergence of the row sums of an infinitesimal row-independent triangular array ( _nj ) of stochastic processes, indexed by a set S, to a sample-continuous Gaussian process, when the array satisfies a random entropy condition, analogous to one used by Giné and Zinn (1984) for empirical processes. This entropy condition is satisfied when S is a class of sets or functions with the Vapnik-ervonenkis property and each _nj (f)fdnj is of the form njc for some reasonable random finite signed measure v _nj. As a result we obtain necessary and sufficient conditions for the weak convergence of (possibly non-i.i.d.) partial-sum processes, and new sufficient conditions for empirical processes, indexed by Vapnik-ervonenkis classes. Special cases include Prokhorov's (1956) central limit theorem for empirical processes, and Shorack's (1979) theorems on weighted empirical processes.Research supported by an NSF Postdoctoral Fellowship, grant no. MCS83-111686     

Central limit theorem of linear regression model under right censorship

In this paper,the estimation of joint dlstribution F(y,z)of(Y,Z)and the estimation in thelinear regression model Y=b'Z+εfor complete data are extended to that of the right censored data.Theregression parameter estimates of b and the variance of ε are weighted least square estimates with randomweights. The central limit theorems of the estimators are obtained under very weak conditions and the derivedasymptotic variance has a very simple form.     

Central limit theorems for generalized set-valued random variables

We give central limit theorems for generalized set-valued random variables whose level sets are compact both in or in a Banach space under milder conditions than those obtained recently by the latter two authors.     

On the global central limit theorem forM-dependent random variables

Published in Lietuvos Matematikos Rinkinys, Vol. 34, No. 2, pp. 259–265, April–June, 1994.