• Communications for Statistical Applications and Methods
• /
• v.15 no.6
• /
• pp.959-967
• /
• 2008

In this paper we show the weak convergence of the linear random(multistochastic process) field generated by identically distributed 2-parameter array of associated random variables. Our result extends the result in Newman and Wright (1982) to the linear 2-parameter processes as well as the result in Kim and Ko (2003) to the 2-parameter case.

• Honam Mathematical Journal
• /
• v.36 no.3
• /
• pp.679-688
• /
• 2014

The notion of asymptotically negative dependence for collection of random variables is generalized to a Hilbert space and the almost sure convergence for these H-valued random variables is obtained. The result is also applied to a linear process generated by H-valued asymptotically negatively dependent random variables.

• Journal of the Chungcheong Mathematical Society
• /
• v.11 no.1
• /
• pp.35-44
• /
• 1998

Let {X(t), $t{\in}\mathbb{R}^n$} be a $S{\alpha}S$ H-sssis Chentsov random field with control measure m. We consider a geometric construction for L$\acute{e}$vy-Chentsov random fields and Takenaka random fields. Finally, we proved some property of conjugate classes and a.s. H$\ddot{o}$lder unboundedness of $S{\alpha}S$ H-sssis Chentsov random fields for all order ${\gamma}$ > H.

• Bulletin of the Korean Mathematical Society
• /
• v.40 no.2
• /
• pp.269-279
• /
• 2003

We prove an empirical LIL for the Kaplan-Meier integral process constructed from the random censorship model under bracketing entropy and mild assumptions due to censoring effects. The main method in deriving the empirical LIL is to use a weak convergence result of the sequential Kaplan-Meier integral process whose proofs appear in Bae and Kim [2]. Using the result of weak convergence, we translate the problem of the Kaplan Meier integral process into that of a Gaussian process. Finally we derive the result using an empirical LIL for the Gaussian process of Pisier [6] via a method adapted from Ossiander [5]. The result of this paper extends the empirical LIL for IID random variables to that of a random censorship model.

• Journal of the Korean Mathematical Society
• /
• v.39 no.1
• /
• pp.119-126
• /
• 2002

For a stationary multivariate linear process of the form X$_{t}$ = (equation omitted), where {Z$_{t}$ : t = 0$\pm$1$\pm$2ㆍㆍㆍ} is a sequence of stationary linearly positive quadrant dependent m-dimensional random vectors with E(Z$_{t}$) = O and E∥Z$_{t}$∥ $^2$< $\infty$, we prove a central limit theorem.theorem.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2006.05a
• /
• pp.201-202
• /
• 2006

To analyze the dynamic characteristics of the bundle drawing process, we employed a Random Phase Spectrum method to generate stochastic test signals that had a given autocorrelation function. And the spectra of the dynamics of the process outputs were obtained, based on the dynamic model of the bundle drawing process. Results showed that the RPS method was very effective to generate stochastic signals that had an exponential function form. The drawing process had the traits that there existed a special frequency range, incurring the process resonance.

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.14 no.6
• /
• pp.1426-1435
• /
• 1990

Dynamic behaviour of point tracking system mounted on flying vehicle shaking in a random manner is investigated and the system dynamic is represented by nonlinear stochastic equations. 2-D.O.F. nonlinear stochastic equations are successfully transformed to linear stochastic equations via equivalent linearization procedure in stochastic sense. Newly developed hybrid technique is used to obtain response statistics of the system under non-white random excitation, which is proved to be remarkably accurate one by performing stochastic simulation.

• Proceedings of the Earthquake Engineering Society of Korea Conference
• /
• 2001.09a
• /
• pp.243-250
• /
• 2001

Industrial machines are sometimes exposed to the danger of earthquake. In the design of a mechanical system, this factor should be accounted for from the viewpoint of reliability. A method to analyze a complex nonlinear structure system under random excitation is proposed. First, the actual random excitation, such as earthquake, is approximated to the corresponding Gaussian process far the statistical analysis. The modal equations of overall system are expanded sequentially. Then, the perturbed equations are synthesized into the overall system and solved in probabilistic way. Several statistical properties of a random process that are of interest in random vibration applications are reviewed in accordance with nonlinear stochastic problem. The obtained statistical properties of the nonlinear random vibration are evaluated in each substructure. Comparing with the results of the numerical simulation proved the efficiency of the proposed method.

• Communications for Statistical Applications and Methods
• /
• v.24 no.5
• /
• pp.457-480
• /
• 2017

We develop a random partition procedure based on a Dirichlet process prior with Laplace distribution. Gibbs sampling of a Laplace mixture of linear mixed regressions with a Dirichlet process is implemented as a random partition model when the number of clusters is unknown. Our approach provides simultaneous partitioning and parameter estimation with the computation of classification probabilities, unlike its counterparts. A full Gibbs-sampling algorithm is developed for an efficient Markov chain Monte Carlo posterior computation. The proposed method is illustrated with simulated data and one real data of the energy efficiency of Tsanas and Xifara (Energy and Buildings, 49, 560-567, 2012).

• Journal of the Korean Statistical Society
• /
• v.24 no.1
• /
• pp.141-147
• /
• 1995

A diffusion model for a system subject to random shocks is introduced. It is assumed that the state of system is modeled by a Brownian motion with negative drift and an absorbing barrier at the origin. It is also assumed that the shocks coming to the system according to a Poisson process decrease the state of the system by a random amount. It is further assumed that a repairman arrives according to another Poisson process and repairs or replaces the system i the system, when he arrives, is in state zero. A forward differential equation is obtained for the distribution function of X(t), the state of the systme at time t, some boundary conditions are discussed, and several interesting characteristics are derived, such as the first passage time to state zero, F(0,t), the probability of the system being in state zero at time t, and F(0), the limit of F(0,t) as t tends to infinity.