• Journal of the Korean Data and Information Science Society
• /
• v.12 no.2
• /
• pp.35-45
• /
• 2001

We introduce a new percentile envelope for diagnosing supernormality in regression analysis. Furthermore, we compare this percentile envelope, which is much simpler and easier, with Atkinson's and Flack and Flores' envelopes. Using percentile envelope, we investigate characteristics of normal probability plots with envelope for error distributions when supernormality is occurred. We give cautions that test result for normality assumption of errors can be reached the wrong conclusion by supernormality.

• Proceedings of the Korean Information Science Society Conference
• /
• 2002.04b
• /
• pp.289-291
• /
• 2002

Host of source separation methods focus on stationary sources so higher-order statistics is necessary In this paler we consider a problem of source separation when sources are second-order nonstationary stochastic processes . We employ the natural gradient method and develop learning algorithms for both 1inear feedback and feedforward neural networks. Thus our algorithms possess equivariant property Local stabi1iffy analysis shows that separating solutions are always locally stable stationary points of the proposed algorithms, regardless of probability distributions of

• Journal of Electrical Engineering and information Science
• /
• v.2 no.6
• /
• pp.123-130
• /
• 1997

This paper presents a generalized expansion method for calculating reliability index in power generation systems. This generalized expansion with a gamma distribution is a very useful tool for the approximation of capacity outage probability distribution of generation system. The well-known Gram-Charlier expansion and Legendre series are also studied in this paper to be compared with this generalized expansion using a sample system IEEE-RTS(Reliability Test System). The results show that the generalized expansion with a composite of gamma distributions is more accurate and stable than Gram-Charlier expansion and Legendre series as addition of the terms to be expanded.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.11 no.17
• /
• pp.9-14
• /
• 1988

A (s, S) inventory policy is studied for a continuous inventory model in which lead times are dependent on the ordering quantity. The model assumes that at most one order is outstanding and demands occur in a compound poison process. The steady-state probability distributions of the inventory levels are derived so as to determine the long-run expected average cost. And the computational procedure is presented.

• Communications for Statistical Applications and Methods
• /
• v.1 no.1
• /
• pp.41-51
• /
• 1994

We consider nonparametric procedures for the selection and ranking problems. Tukey's generalized lambda distribution is condidered as the distribution for the score function because the distribution can approximate many well-known contionuous distributions. Also we compare these procedures in terms of efficiency, defined by the ratio of a probability of a correct selection divided by the expected selected subset size.

• Journal of the Korean Statistical Society
• /
• v.11 no.1
• /
• pp.45-58
• /
• 1982

The problem of comparing k normal populations with a control (or a standard) in terms of the absolute values of their means is considered. Under the framework of indifference-zone formulation a single-state and a two-stage procedures for selecting the best are proposed, according to their commom vairances known or unknown respectively. The procedures guarantee that the probability of correct selection is not less than some preassigned lower limit. Selected tables necessary to implement the procedures are provided.

• Journal of the Korean Mathematical Society
• /
• v.56 no.2
• /
• pp.457-473
• /
• 2019

In this paper, we investigate weak laws of large numbers for weighted coordinatewise pairwise negative quadrant dependence random vectors in Hilbert spaces in the case that the decay order of tail probability is r for some 0 < r < 2. Moreover, we extend results concerning Pareto-Zipf distributions and St. Petersburg game.

• Journal of the Korean Mathematical Society
• /
• v.61 no.4
• /
• pp.693-711
• /
• 2024

A Markov-modulated Bernoulli process is a generalization of a Bernoulli process in which the success probability evolves over time according to a Markov chain. It has been widely applied in various disciplines for modeling and analysis of systems in random environments. This paper focuses on providing analytical characterizations of the Markovmodulated Bernoulli process by introducing key metrics, including success period, failure period, and cycle. We derive expressions for the distributions and the moments of these metrics in terms of the model parameters.

• Korean Journal of Food Science and Technology
• /
• v.42 no.3
• /
• pp.373-376
• /
• 2010

The objective of this study was to present the proper probability distribution model based on the data obtained from surveys on domestic refrigerator food storage temperatures in home. Domestic refrigerator temperatures were determined as risk factors in foodborne disease outbreaks for microbial risk assessment (MRA). The temperature was measured by directly visiting 139 homes using a data logger from May to September of 2009. The overall mean temperature for all the refrigerators in the survey was $3.53{\pm}2.96^{\circ}C$, with 23.6% of the refrigerators measuring above $5^{\circ}C$. Probability distributions were also created using @RISK program based on the measured temperature data. Statistical ranking was determined by the goodness of fit (GOF, i.e., the Kolmogorov-Smirnov (KS) or Anderson-Darling (AD) test) to determine the proper probability distribution model. This result showed that the LogLogistic (-10.407, 13.616, 8.6107) distribution was found to be the most appropriate for the MRA model. The results of this study might be directly used as input variables in exposure evaluation for conducting MRA.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.48 no.7
• /
• pp.537-546
• /
• 2020

The reliability used as a performance indicator during the development of space launch vehicle should be validated by the launch success probability, and the launch data need to be fed back for reliability management. In this paper, the launch data of space launch vehicles around the world were investigated and statistically analyzed for the success probabilities according to the launch vehicle models and maturity. The Bayesian estimation of launch success probability was reviewed and analyzed by comparing the estimated success probabilities using several prior distributions and the statistical success probability. We presented the method of generating prior distribution function and considerations for Bayesian estimation.