• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 1998.10a
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• pp.338-343
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• 1998

This study is to select appropriate probability distributions for ten days evaporation data for the purpose of representing statistical characteristics of real evaporation data in Korea. Nine probability distribution functions were assumed to be underlying distributions for ten days evaporation data of 20 stations with the duration of 20 years. The parameter of each probability distribution function were estimated by the maximum likelihood approach, and appropriate probability distributions were selected from the goodness of fit test. Log Pearson type III model was selected as an appropriate probability distribution for ten days evaporation data in Korea.

• Journal of Korean Society of Water and Wastewater
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• v.22 no.6
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• pp.609-617
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• 2008

Water pipes are supposed to deliver the predetermined demand safely to a certain point in water distribution system. However, pipe burst or crack can be happened due to so many reasons such as the water hammer, natural pipe ageing, external impact force, soil condition, and various environments of pipe installation. In the present study, the reliability model which can calculate the probability of pipe breakage was developed regarding unsteady effect such as water hammer. For the reliability model, reliability function was formulated by Barlow formula. AFDA method was applied to calculate the probability of pipe breakage. It was found that the statistical distribution for internal pressure among the random variables of reliability function has a good agreement with the Gumbel distribution after unsteady analysis was performed. Using the present model, the probability of pipe breakage was quantitatively calculated according to random variables such as the pipe diameter, thickness, allowable stress, and internal pressure. Furthermore, it was found that unsteady effect significantly increases the probability of pipe breakage. If this reliability model is used for the design of water distribution system, safe and economical design can be accomplished. And it also can be effectively used for the management and maintenance of water distribution system.

• The Korean Journal of Applied Statistics
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• v.23 no.6
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• pp.1057-1065
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• 2010

In this paper we discuss some cautionary notes in using the Pearson chi-squared test statistic for the goodness-of-fit of the ordinal response model. If a model includes continuous type explanatory variables, the resulting table from the t of a model is not a regular one in the sense that the cell boundaries are not fixed but randomly determined by some other criteria. The chi-squared statistic from this kind of table does not have a limiting chi-square distribution in general and we need to be very cautious of the use of a chi-squared type goodness-of-t test. We also study the limiting distribution of the chi-squared type statistic for testing the goodness-of-t of cumulative logit models with ordinal responses. The regularity conditions necessary to the limiting distribution will be reformulated in the framework of the cumulative logit model by modifying those of Moore and Spruill (1975). Due to the complex limiting distribution, a parametric bootstrap testing procedure is a good alternative and we explained the suggested method through a practical example of an ordinal response dataset.

• Journal of the Korean Statistical Society
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• v.26 no.4
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• pp.477-491
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• 1997

A new predictive classification rule for assigning future cases into one of two multivariate normal population (with unknown normal mixture model) is considered. The development involves calculation of posterior probability of each possible normal-mixture model via a default Bayesian test criterion, called intrinsic Bayes factor, and suggests predictive distribution for future cases to be classified that accounts for model uncertainty by weighting the effect of each model by its posterior probabiliy. In this paper, our interest is focused on constructing the classification rule that takes care of uncertainty about the types of covariance matrices (homogeneity/heterogeneity) involved in the model. For the constructed rule, a Monte Carlo simulation study demonstrates routine application and notes benefits over traditional predictive calssification rule by Geisser (1982).

• Journal of the Korean Statistical Society
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• v.25 no.3
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• pp.433-444
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• 1996

In contrast to the multiplicative risk model, the additive risk model specifies that the hazard function with covariates is the sum of, rather than product of, the baseline hazard function and the regression function of covariates. We, in this paper, propose a method for checking the adequacy of the additive risk model based on partial-sum of matingale residuals. Under the assumed model, the asymptotic properties of the proposed test statistic and approximation method to find the critical values of the limiting distribution are studied. Several real examples are illustrated.

• The Korean Journal of Applied Statistics
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• v.11 no.1
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• pp.185-192
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• 1998

A bivariate extension of the two-parameter exponential distribution is proposed as a model for certain problems in system level life testing. In particular, it applies to two-component shared parallel systems having a minimum guarantee time. Various statistical properties of the model are investigated, including maximum likelihood estimators (MLEs), modified MLEs, and unbiased estimators of the parameters and their distributions.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.12
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• pp.2004-2010
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• 2003

Fracture life and crack retardation behavior were examined experimentally using CT specimens of aluminum alloy 5083. Crack retardation life and fracture life were a wide difference. between 0.8 and 0.6 in proportion to ratio of load reduction. The wheeler model retardation parameter was used successfully to predict crack growth behavior. By using a crack propagation rule, prediction of fracture life can be evaluated quantitatively. A statistical approach based on Weibull distribution was applied to the test data to evaluate the dispersion in the retardation life and fracture life by the change of load reduction.

• Journal of the Korean Statistical Society
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• v.26 no.3
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• pp.397-405
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• 1997

Recently maximum likelihood estimators using unconditional likelihood function are used for testing unit roots. When one wants to use this method the determinant term of initial values in the multivariate unconditional likelihood function produces a complicated function of the elements in the coefficient matrix and variance matrix. In this paper an approximation of the determinant term is calculated and based on this aproximation an approximated unconditional likelihood function is calculated. The approximated unconditional maximum likelihood estimators can be used to test for unit roots. When multivariate process has one unit root the limiting distribution obtained by this method and the limiting distribution using exact unconditional likelihood function are the same.

• Communications for Statistical Applications and Methods
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• v.16 no.4
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• pp.697-705
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• 2009

The Pearson chi-squared statistic or the deviance statistic is widely used in assessing the goodness-of-fit of the generalized linear models. But these statistics are not proper in the situation of continuous explanatory variables which results in the sparseness of cell frequencies. We propose a goodness-of-fit test statistic for the cumulative logit models with ordinal responses. We consider the grouping of a dataset based on the ordinal scores obtained by fitting the assumed model. We propose the Pearson chi-squared type test statistic, which is obtained from the cross-classified table formed by the subgroups of ordinal scores and the response categories. Because the limiting distribution of the chi-squared type statistic is intractable we suggest the parametric bootstrap testing procedure to approximate the distribution of the proposed test statistic.

• Communications for Statistical Applications and Methods
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• v.21 no.2
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• pp.115-124
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• 2014

In this paper we derive a Berry-Esseen type bound for the kernel density estimator of a random left truncated model, in which each datum (Y) is randomly left truncated and is sampled if $Y{\geq}T$, where T is the truncation random variable with an unknown distribution. This unknown distribution is estimated with the Lynden-Bell estimator. In particular the normal approximation rate, by choice of the bandwidth, is shown to be close to $n^{-1/6}$ modulo logarithmic term. We have also investigated this normal approximation rate via a simulation study.