• International Journal of Reliability and Applications
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• v.1 no.2
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• pp.133-143
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• 2000

Data from weighted distributions appear, among other situations, when some of the data are missing or are damaged, a case that is important in reliability and life testing. The kernel method for hazard rate estimation is discussed for these data where the basic large sample properties are given. As a by product, the basic properties of the kernel estimate of the distribution function for data from weighted distribution are presented.

• Journal of applied mathematics & informatics
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• v.24 no.1_2
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• pp.221-230
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• 2007

The exponential and the gamma distributions have been the traditional models for drought duration and drought intensity data, respectively. However, it is often assumed that the drought duration and drought intensity are independent, which is not true in practice. In this paper, an application of the bivariate gamma exponential distribution is provided to drought data from Nebraska. The exact distributions of R=X+Y, P=XY and W=X/(X+Y) and the corresponding moment properties are derived when X and Y follow this bivariate distribution.

• Nuclear Engineering and Technology
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• v.41 no.3
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• pp.295-306
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• 2009

The subchannel grade void distributions in the NUPEC (Nuclear Power Engineering Corporation) BFBT (BWR Full-Size Fine-Mesh Bundle Tests) facility were evaluated with the subchannel analysis code MATRA and the system code MARS. Fifteen test series from five different test bundles were selected for an analysis of the steady-state subchannel void distributions. Two transient cases, a turbine trip without a bypass as a typical power transient and a re-circulation pump trip as a flow transient, were also chosen for this analysis. It was found that the steady-state void distributions calculated by both the MATRA and MARS codes coincided well with the measured data in the range of thermodynamic qualities from 5% to 25%. The results of the transient calculations were also similar and were highly feasible. However, the computational aspects of the two codes were clearly different.

• Journal of Korean Society for Quality Management
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• v.34 no.4
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• pp.102-109
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• 2006

This paper investigates the effects of non-normality on the performance of univariate and multivariate cumulative sum(CUSUM) control charts for monitoring the process mean. In-control and out-of-control average run lengths of the charts are examined for the univariate/multivariate lognormal and t distributions. The effects of the reference value and the correlation coefficient under the non-normal distributions are also studied. Simulation results show that the CUSUM charts with small reference values are robust to non-normality but those with moderate or large reference values are sensitive to non-normal data especially to process data from skewed distributions. The performance of the chart to detect mean shift of a process is not invariant to the direction of the shift for skewed distributions.

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.595-598
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• 2004

This research seeks to derive the design rainfalls through the L-moment with the test of homogeneity, independence and outlier of data on annual maximum daily rainfall at 38 rainfall stations in Korea. To select the appropriate distribution of annual maximum daily rainfall data by the rainfall stations, Generalized Extreme Value (GEV), Generalized Logistic (GLO), Generalized Pareto (GPA), Generalized Normal (GNO) and Pearson Type 3 (PT3) probability distributions were applied and their aptness were judged using an L-moment ratio diagram and the Kolmogorov-Smirnov (K-S) test. Parameters of appropriate distributions were estimated from the observed and simulated annual maximum daily rainfall using Monte Carlo techniques. Design rainfalls were finally derived by GEV distribution, which was proved to be more appropriate than the other distributions.

• Journal of The Korean Society of Agricultural Engineers
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• v.46 no.3
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• pp.31-42
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• 2004

This research seeks to derive the design rainfalls through the L-moment with the test of homogeneity, independence and outlier of data on annual maximum daily rainfall at 38 rainfall stations in Korea. To select the appropriate distribution of annual maximum daily rainfall data by the rainfall stations, Generalized Extreme Value (GEV), Generalized Logistic (GLO), Generalized Pareto (GPA), Generalized Normal (GNO) and Pearson Type 3 (PT3) probability distributions were applied and their aptness were judged using an L-moment ratio diagram and the Kolmogorov-Smirnov (K-S) test. Parameters of appropriate distributions were estimated from the observed and simulated annual maximum daily rainfall using Monte Carlo techniques. Design rainfalls were finally derived by GEV distribution, which was proved to be more appropriate than the other distributions.

• Communications for Statistical Applications and Methods
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• v.25 no.1
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• pp.71-78
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• 2018

In this study, we consider the likelihood ratio test for the covariance matrix of the multivariate normal data. For this, we propose a method for obtaining null distributions of the likelihood ratio statistics by the Monte-Carlo approach when it is difficult to derive the exact null distributions theoretically. Then we compare the performance and precision of distributions obtained by the asymptotic normality and the Monte-Carlo method for the likelihood ratio test through a simulation study. Finally we discuss some interesting features related to the likelihood ratio test for the covariance matrix and the Monte-Carlo method for obtaining null distributions for the likelihood ratio statistics.

• Communications of the Korean Mathematical Society
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• v.34 no.4
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• pp.1335-1352
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• 2019

In this article, a new family of lifetime distributions by adding two additional parameters is introduced. The new family is called, the logarithmic Kumaraswamy family of distributions. For the proposed family, explicit expressions for some mathematical properties are derived. Maximum likelihood estimates of the model parameters are also obtained. This method is applied to develop a new lifetime model, called the logarithmic Kumaraswamy Weibull distribution. The proposed model is very flexible and capable of modeling data with increasing, decreasing, unimodal or modified unimodal shaped hazard rates. To access the behavior of the model parameters, a simulation study has been carried out. Finally, the potentiality of the new method is proved via analyzing two real data sets.

• Wind and Structures
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• v.19 no.5
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• pp.467-480
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• 2014

The estimation of wind speed values used in codes and standards is an integral part of the wind load evaluation process. In a number of codes and standards, wind speeds outside of tropical cyclone prone regions are estimated using a single probability distribution developed from observed wind speed data, with no distinction made between the types of causal wind hazard (e.g., thunderstorm). Non-tropical cyclone wind hazards (i.e., thunderstorm, non-thunderstorm) have been shown to possess different probability distributions and estimation of non-tropical cyclone wind speeds based on a single probability distribution has been shown to underestimate wind speeds. Current treatment of non-tropical cyclone wind hazards in worldwide codes and standards is touched upon in this work. Meteorological data is available at a considerable number of United States (U.S.) stations that have information on wind speed as well as the type of causal wind hazard. In this paper, probability distributions are fit to distinct storm types (i.e., thunderstorm and non-thunderstorm) and the results of these distributions are compared to fitting a single probability distribution to all data regardless of storm type (i.e., co-mingled). Distributions fitted to data separated by storm type and co-mingled data will also be compared to a derived (i.e., "mixed") probability distribution considering multiple storm types independently. This paper will analyze two extreme value distributions (e.g., Gumbel, generalized Pareto). It is shown that mixed probability distribution, on average, is a more conservative measure for extreme wind speed estimation. Using a mixed distribution is especially conservative in situations where a given wind speed value for either storm type has a similar probability of occurrence, and/or when a less frequent storm type produces the highest overall wind speeds. U.S. areas prone to multiple non-tropical cyclone wind hazards are identified.

• Communications for Statistical Applications and Methods
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• v.22 no.3
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• pp.233-239
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• 2015

This study compares two generalized Lindley distributions and assesses consistency between theoretical and analytical results. Data (complete and censored) assumed to follow the Lindley distribution are generated and analyzed using two generalized Lindley distributions, and maximum likelihood estimates of parameters from the generalized distributions are obtained. Size and power of tests of hypotheses on the parameters are assessed drawing on asymptotic properties of the maximum likelihood estimates. Results suggest that whereas size of some of the tests of hypotheses based on the considered generalized distributions are essentially ${\alpha}$-level, some are possibly not; power of tests of hypotheses on the Lindley distribution parameter from the two distributions differs.