• Communications for Statistical Applications and Methods
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• v.16 no.4
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• pp.647-658
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• 2009

It is already known from the previous study that flood seems to have heavier tail. Therefore, to make prediction of future extreme label, some agreement of tail behavior of extreme data is highly required. The LH-moments estimation method, the generalized form of L-moments is an useful method of characterizing the upper part of the distribution. LH-moments are based on linear combination of higher order statistics. In this study, we have formulated LH-moments of five distributions useful in hydrology such as, two types of three parameter kappa distributions, beta-${\kappa}$ distribution, beta-p distribution and a generalized Gumbel distribution. Using LH-moments reduces the undue influences that small sample may have on the estimation of large return period events.

• Advances in nano research
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• v.7 no.3
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• pp.191-208
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• 2019

In the present work the dynamic analysis of the functionally graded rectangular nanoplates is studied. The theory of nonlocal elasticity based on the quasi 3D high shear deformation theory (quasi 3D HSDT) has been employed to determine the natural frequencies of the nanosize FG plate. In HSDT a cubic function is employed in terms of thickness coordinate to introduce the influence of transverse shear deformation and stretching thickness. The theory of nonlocal elasticity is utilized to examine the impact of the small scale on the natural frequency of the FG rectangular nanoplate. The equations of motion are deduced by implementing Hamilton's principle. To demonstrate the accuracy of the proposed method, the calculated results in specific cases are compared and examined with available results in the literature and a good agreement is observed. Finally, the influence of the various parameters such as the nonlocal coefficient, the material indexes, the aspect ratio, and the thickness to length ratio on the dynamic properties of the FG nanoplates is illustrated and discussed in detail.

• Journal of the Korean Data and Information Science Society
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• v.28 no.4
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• pp.733-742
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• 2017

There are many areas of applications where Gumbel distribution are employed such as environmental sciences, system reliability and hydrology. The goodness-of-fit test for Gumbel distribution is very important in environmental sciences, system reliability and hydrology data analysis. Therefore, we propose the two test statistics to test goodness-of-fit for the Gumbel distribution based on the generalized Lorenz curve. We compare the new test statistic with the Anderson - Darling test, Cramer - vonMises test, and modified Anderson - Darling test in terms of the power of the test through by Monte Carlo method. As a result, the new test statistics are more powerful than the other test statistics. Also, we propose new graphic method to goodness-of-fit test for the Gumbel distribution based on the generalized Lorenz curve.

• Communications for Statistical Applications and Methods
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• v.12 no.2
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• pp.443-451
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• 2005

The studied in this paper is a new algorithm for searching the maximum likelihood estimate(MLE) in which probability density function is not explicitly expressed. Newton-Raphson's root-finding routine and a nonlinear numerical optimization algorithm with constraint (so-called feasible sequential quadratic programming) are used. This algorithm is applied to the Wakeby distribution which is importantly used in hydrology and water resource research for analysis of extreme rainfall. The performance comparison between maximum likelihood estimates and method of L-moment estimates (L-ME) is studied by Monte-carlo simulation. The recommended methods are L-ME for up to 300 observations and MLE for over the sample size, respectively. Methods for speeding up the algorithm and for computing variances of estimates are discussed.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.15-21
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• 2007

Due to a decreasing tendency of river runoff in the Laohahe River basin in North China, quantitative analysis was made with the aid of the conceptual Xinanjiang model under the background of nature climate variability as well as human-induced climate change according to the long-term observational hydrometeorological data. In the past, the human effect on surface water resources was estimated by investigating the impact of human activities on each item in the equation of water balance, so as to calculate water quantity of each item in the original natural status. It seems to be clear conceptually. It is appropriate just for the case of direct impact, such as water transfer from one basin to another, water storage by various scales of hydraulic projects, besides a huge amount of investigation and indeterminate statistics data when applied in practice. It is difficult for us to compute directly water consumption due to the implementation of measures for soil conservation, the improvement of farming techniques in agriculture, the growth of population in towns and villages, and the change of socioeconomic structure. In view of such situation, the Xinanjiang model was used to separate human impact from the climatic impact on water resources. Quantitatively human activity made river runoff decrease by 1.02, 50.67, 58.06 mm in 1960's, 1970's, 1980's, respectively, while by 97.2 mm in 1990's in the sense of annual average in the Laohahe River basin.

• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.535-545
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• 2015

In a various range of applications including hydrology, the type-I extreme value distribution has been extensively used as a probabilistic model for analyzing extreme events. In this paper, we introduce methods for estimating the scale parameter of the type-I extreme value distribution. A simulation study is performed to compare the estimators in terms of mean-squared error and bias, and the obtained results are provided.

• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.19-30
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• 2003

Spatiotemporal statistical models are used for analyzing space-time data in many fields, such as environmental sciences, meteorology, geology, epidemiology, forestry, hydrology, fishery, and so on. It is well known that classical spatiotemporal process modeling requires the estimation of space-time variogram or covariance functions. In practice, the estimation of such variogram or covariance functions are computationally difficult and highly sensitive to data structures. We investigate a Bayesian hierarchical model which allows the specification of a more realistic series of conditional distributions instead of computationally difficult and less realistic joint covariance functions. The spatiotemporal model investigated in this study allows both spatial component and autoregressive temporal component. These two features overcome the inability of pure time series models to adequately predict changes in trends in individual sites.

• Communications for Statistical Applications and Methods
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• v.25 no.3
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• pp.245-261
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• 2018

Attempts have been made to define new classes of distributions that provide more flexibility for modelling skewed data in practice. In this work we define a new extension of the generalized gamma distribution (Stacy, The Annals of Mathematical Statistics, 33, 1187-1192, 1962) for Marshall-Olkin generalized gamma (MOGG) distribution, based on the generator pioneered by Marshall and Olkin (Biometrika, 84, 641-652, 1997). This new lifetime model is very flexible including twenty one special models. The main advantage of the new family relies on the fact that practitioners will have a quite flexible distribution to fit real data from several fields, such as engineering, hydrology and survival analysis. Further, we also define a MOGG mixture model, a modification of the MOGG distribution for analyzing lifetime data in presence of cure fraction. This proposed model can be seen as a model of competing causes, where the parameter associated with the Marshall-Olkin distribution controls the activation mechanism of the latent risks (Cooner et al., Statistical Methods in Medical Research, 15, 307-324, 2006). The asymptotic properties of the maximum likelihood estimation approach of the parameters of the model are evaluated by means of simulation studies. The proposed distribution is fitted to two real data sets, one arising from measuring the strength of fibers and the other on melanoma data.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.09a
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• pp.782-789
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• 2010

Landslides cause enormous economic losses and casualties. Korea has mountainous regions and heavy slopes in most parts of the land and has consistently built new roads and large-scale housing complexes according to its industrial and urban growth. As a result, the damage from landslides becomes greater every year. In this study, performed a GIS-based landslide hazard analysis by SINMAP(Stability Index MAPping) model in Gyeonggi Icheon area coupling with geomorphological and geological data. SINMAP model has its theoretical basis in the infinite plane slope stability model with wetness obtained from a topographically based steady state model of hydrology. To Gyeonggi Icheon area landslides hazards evaluated, these SINMAP model were analysed results while simultaneously referring to the stability index map, where lines distinguish the zones categorized into the different stability classes and a table giving summary statistics.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.991-993
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• 2003

The 4 sets of environmental variables dealing with meteorology, hydrology and physiography were analyzed to generate a spatial drought risk index of Phitsanulok province of Thailand. The analysis of K-mean and discriminant were applied to the set of the selective drought variables for grouping each of spatial variable set into 4 classes. The obtained 4 classes, based on group statistics, were thus recoded in the meaning of no risk, low risk, moderate risk, and high risk. The regression coefficient between recoded classes and a set of the selective environmental variables were then applied as spatial variable weighting on thematic dataset in GIS spatial analysis. The results showed that the weighting score of drought variable was highest in meteorological variable compared to other variables.