• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• v.3 no.1
• /
• pp.107-114
• /
• 1999

In many applications (e,g., identification of non-destructive testing signal and biomedical signal and multiscale analysis of image), it is of interest to analyze and identify phenomena occurring at the different scales. The recently introduced wave let transforms provide a time-scale decomposition of signals that offers the possibility of such signals. However, there is no corresponding statistical properties to development of multiscale statistical signal processing. In this paper, we derive such properties of random signals in wavelet domain.

• Journal of Korean Institute of Industrial Engineers
• /
• v.40 no.5
• /
• pp.477-480
• /
• 2014

The single-period production inventory control problem under random yield is considered to analyze the impact of the yield characteristics on the firm's profit. We use the stochastic comparison as a main vehicle to compare the profits resulted under different random yields. Commonly used stochastic orderings are addressed with an analysis of their implications on the firm's profit. Moreover, a distribution-free bound on the profit is derived.

• Communications for Statistical Applications and Methods
• /
• v.18 no.5
• /
• pp.595-601
• /
• 2011

A community of the social network refers to the subset of nodes linked more densely among them than to others. In this study, we propose a Monte-Carlo method for generating random social unipartite and bipartite networks with two or more communities. Proposed random networks can be used to verify the small world phenomenon of the social networks with several communities.

• Journal of applied mathematics & informatics
• /
• v.16 no.1_2
• /
• pp.483-488
• /
• 2004

Results on the analytic behavior of the limiting spectral distribution of large dimensional random matrices, studied in Marcenko and Pastur [2], are derived. Using the Stieltjes transform, it is shown that the limiting distribution has a continuous derivative away from zero, the derivative being analytic whenever it is positive [3]. In the present paper, it is derived that the behavior of it resembles the behavior of a square root function near the boundary of its support.

• Composites Research
• /
• v.12 no.5
• /
• pp.23-30
• /
• 1999

Fiber reinforced composite materials are widely used in automotive industry to produce parts that are large, thin, lightweight, strong and stiff. It is very important to know a charge shape in order to have good products in the compression molding. In particular, the product such as a bumper beam is composed of the random and unidirectional fiber mats. The characteristics of flow fronts such as a bulging phenomenon for random mat and unidirectional fiber mat and slip parameters are studied numerically. And the effects of viscosity ratio and stack type on mold filling parameters are also discussed.

• Communications for Statistical Applications and Methods
• /
• v.7 no.3
• /
• pp.817-827
• /
• 2000

In this paper, we investigate the Bayesian approach to random effect binomial regression models with improper prior due to the absence of information on parameter. We also propose a method of estimating the posterior moments and prediction and discuss some general methods for studying model assessment. The methodology is illustrated with Crowder's Seeds Data. Markov Chain Monte Carlo techniques are used to overcome the computational difficulties.

• Journal of the Korean Statistical Society
• /
• v.34 no.1
• /
• pp.61-71
• /
• 2005

A class of asymmetric ARCH processes is proposed via binary random power transformations. This class accommodates traditional nonlinear models such as threshold ARCH (Rabemanjara and Zacoian (1993)) and Box-Cox type ARCH models(Higgins and Bera (1992)). Stationarity condition of the model is addressed. Iterative least squares(ILS) and pseudo maximum like-lihood(PML) methods are discussed for estimating parameters and related algorithms are presented. Illustrative analysis for Korea Stock Prices Index (KOSPI) data is conducted.

• Journal of the Korean Data and Information Science Society
• /
• v.17 no.1
• /
• pp.95-113
• /
• 2006

The data resulting from a deterministic dynamic system may often appear to be random. However, it is important to distinguish a deterministic and a random processes for statistical analysis. In this paper, we propose a nonparametric test procedure to distinguish a noisy chaos from i.i.d. random process. The proposed procedure can be easily implemented by computer. We notice that the test is very effective to identify a low dimensional chaos process in some cases.

• Journal of the Korean Data and Information Science Society
• /
• v.15 no.2
• /
• pp.405-411
• /
• 2004

In the analysis of cancer data, it is important to make inferences of survival function and to assess the effects of covariates. Cox's proportional hazard model(PHM) and Beran's nonparametric method are generally used to estimate the survival function with covariates. We adjusted the incomplete survival time using the Buckley and James's(1979) pseudo random variables, and then proposed the estimator for the conditional survival function. Also, we carried out the simulation studies to compare the performances of the proposed method.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.60 no.3
• /
• pp.63-70
• /
• 2018

In this study, a simple probabilistic approach using equivalent coefficient of consolidation ($c_e$) was proposed to consider the spatial variability of coefficient of vertical consolidation ($c_v$), and the effect of the probability distribution of coefficient of consolidation on degree of consolidation in heterogeneous soil was investigated. The statistical characteristics of consolidation coefficient were estimated from 1,226 field data, and four probability distributions (Normal, Log-normal, Gamma, and Weibull) were applied to consider the effect of probability distribution. The random fields of coefficient of consolidation were generated based on Karhunen-Loeve expansion. Then, the equivalent coefficient of consolidation was calculated from the random field and used as the input value of consolidation analysis. As a result, the probabilistic analysis can be performed effectively by separating random field and numerical analysis, and probabilistic analysis was performed using a Latin hypercube Monte Carlo simulation. The results showed that the statistical properties of $c_e$ were changed by the probability distribution and spatial variability of $c_v$, and the probability distribution of $c_v$ has considerable effects on the probabilistic results. There was a large difference of failure probability depend on the probability distribution when the autocorrelation distance was small (i.e., highly heterogeneous soil). Therefore, the selection of a suitable probability distribution of $c_v$ is very important for reliable probabilistic analysis of consolidation.