• Communications for Statistical Applications and Methods
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• v.12 no.3
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• pp.545-556
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• 2005

Hjort(1990) obtained the nonparametric Bayes estimator $\^{F}_{c,a}$ of $F_0$ with respect to beta processes in the random censorship model. Let $X_1,{\cdots},X_n$ be i.i.d. $F_0$ and let $C_1,{\cdot},\;C_n$ be i.i.d. G. Assume that $F_0$ and G are continuous. This paper shows that {$\^{F}_{c,a}$(u){\|}0 < u < T} converges weakly to a Gaussian process whenever T < $\infty$ and $\~{F}_0({\tau})\;<\;1$.

• Journal of the Korean Mathematical Society
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• v.36 no.4
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• pp.737-746
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• 1999

We consider a class of discrete parameter processes on a locally compact Banach space S arising from successive compositions of strictly stationary random maps with state space C(S,S), where C(S,S) is the collection of continuous functions on S into itself. Sufficient conditions for stationary solutions are found. Existence of pth moments and convergence of empirical distributions for trajectories are proved.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1990.04a
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• pp.24-30
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• 1990

An improved algorithm for simulation of multi-variate random processes has been presented. It is based on the spectral representation method. The conventional methods give sample time histories which satisfy the target spectral density matrix only in the sense of ensemble average. However, the present method can generate sample functions which satisfy the target spectra in the ergodic sense. Example analysis is given for the simulation of earthquake accelerations with three components.

• Journal of the Korean Data and Information Science Society
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• v.16 no.1
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• pp.165-172
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• 2005

In recently, Popova and Wu(1999) proved a theorem which presents the long-run average fuzzy reward per unit time. In this note, we improve this result. Indeed we will show uniform convergence of a renewal reward processes with respect to the level ${\alpha}$ modeled as a fuzzy random variables.

• Communications for Statistical Applications and Methods
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• v.21 no.3
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• pp.245-251
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• 2014

Two exponential inequalities are established for a wide class of general weakly dependent sequences of random variables, called ${\psi}$-weakly dependent process which unify weak dependence conditions such as mixing, association, Gaussian sequences and Bernoulli shifts. The ${\psi}$-weakly dependent process includes, for examples, stationary ARMA processes, bilinear processes, and threshold autoregressive processes, and includes essentially all classes of weakly dependent stationary processes of interest in statistics under natural conditions on the process parameters. The two exponential inequalities are established on more general conditions than some existing ones, and are proven in simpler ways.

• Communications of the Korean Mathematical Society
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• v.25 no.1
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• pp.119-128
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• 2010

Let ${X_n,\;n\geq1}$ be a sequence of independent identically distributed (i.i.d.) random variables (r.vs.), defined on a probability space ($\Omega$,A,P), and let ${N_n,\;n\geq1}$ be a sequence of positive integer-valued r.vs., defined on the same probability space ($\Omega$,A,P). Furthermore, we assume that the r.vs. $N_n$, $n\geq1$ are independent of all r.vs. $X_n$, $n\geq1$. In present paper we are interested in asymptotic behaviors of the random sum $S_{N_n}=X_1+X_2+\cdots+X_{N_n}$, $S_0=0$, where the r.vs. $N_n$, $n\geq1$ obey some defined probability laws. Since the appearance of the Robbins's results in 1948 ([8]), the random sums $S_{N_n}$ have been investigated in the theory probability and stochastic processes for quite some time (see [1], [4], [2], [3], [5]). Recently, the random sum approach is used in some applied problems of stochastic processes, stochastic modeling, random walk, queue theory, theory of network or theory of estimation (see [10], [12]). The main aim of this paper is to establish some results related to the asymptotic behaviors of the random sum $S_{N_n}$, in cases when the $N_n$, $n\geq1$ are assumed to follow concrete probability laws as Poisson, Bernoulli, binomial or geometry.

• Journal of the Korean Mathematical Society
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• v.33 no.2
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• pp.319-332
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• 1996

Many authors have studied multiple stochastic integrals in pursuit of the existence of product processes in terms of multiple integrals. But there has not been much research into the structure of the product processes themselves. In this direction, a study which gives emphasis on sample path continuity and boundedness properties was initiated in Pyke[9]. For details of problem set-ups and necessary notations, see [9]. Recently the weak limits of U-processes are shown to be chaos processes, which is product of the same Brownian measures, see [2] and [7].

• Journal of the Korean Mathematical Society
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• v.35 no.3
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• pp.575-581
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• 1998

We are interested in random phenomena that will vary as time goes by, being interfered with by fluctuation. These phenomena are often expressed as functionals of white noise. We therefore discuss the analysis of those functionals, where the white noise is understood as a system of idealized elementary random variables. The system is, in many cases, taken to be the innovation of the given random phenomena. The use of the innovation provides a powerful tool to investigate stochastic processes and random fields in line with white noise analysis.

• Proceedings of the Korean Society of Computational and Applied Mathematics Conference
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• 2003.09a
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• pp.8.2-8
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• 2003

The theory of fuzzy random variables and fuzzy stochastic processes has been received much attentions in recent years. But convergence in distribution for fuzzy random variables has not established yet. In this talk, we restrict our concerns to level-wise continuous fuzzy random variables and obtain some characterizations of its tightness and convergence in distribution.

• Journal of applied mathematics & informatics
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• v.29 no.5_6
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• pp.1351-1361
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• 2011

We in this paper study the complete convergence and almost surely convergence for arrays of rowwise pairwise negatively dependent(ND) random variables (r.${\upsilon}$.'s) which are dominated randomly by some random variables and obtain a result dealing with complete convergence of linear processes.