• Title/Summary/Keyword: Distribution Department

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A Bivariate Two Sample Rank Test for Mixture Distributions

  • Songyong Sim;Seungmin Lee
    • Communications for Statistical Applications and Methods
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    • v.3 no.2
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    • pp.197-204
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    • 1996
  • We consider a two sample rank test for a bivariate mixture distribution based on Johnson's quantile score. The test statistic is simple to calculate and the exact distribution under the null hypothesis is obtained. A numerical example is given.

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A Study on Design Support Technique for Water Distribution Network using GIS (GIS를 이용한 상수관로 설계지원 기법 연구)

  • Cho, Hyo-Seob;Choi, Seung-Chul;Lee, Gi-Ha;Cho, Bok-Hwan;Kim, Jeong-Yup
    • Journal of the Korean Association of Geographic Information Studies
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    • v.8 no.2
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    • pp.103-116
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    • 2005
  • Although there have been many researches to construct a database of water distribution networks using GIS, most of them were not linked with an model for the analysis of pipe networks because it is difficult to make spatial data about complex water distribution networks for building a detail model. Therefore, it is necessary to develop the method based on GIS to build geographical data for design of water distribution pipeline systems. In this study, an innovated design support technique using GIS is proposed for a hydraulic analysis model of water distribution networks. With the function of spatial analysis in GIS system, the results from a pipe network model are used to analyze the suitability of the location of pipeline network, the spatial suitability comprised the analysis of the degree of pipe age, the altitude distribution of water pressure, and the water supply system for the customer.

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Multivariate empirical distribution functions and descriptive methods (다변량 경험분포함수와 시각적인 표현방법)

  • Hong, Chong Sun;Park, Jun;Park, Yong Ho
    • Journal of the Korean Data and Information Science Society
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    • v.28 no.1
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    • pp.87-98
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    • 2017
  • The multivaiate empirical distribution function (MEDF) is defined in this work. The MEDF's expectation and variance are derived and we have shown the MEDF converges to its real distribution function. Based on random samples from bivariate standard normal distribution with various correlation coefficients, we also obtain MEDFs and propose two kinds of graphical methods to visualize MEDFs on two dimensional plane. One is represented with at most n stairs with similar arguments as the step function, and the other is described with at most n curves which look like bivariate quantile vector. Even though these two descriptive methods could be expressed with three dimensional space, two dimensional representation is obtained with ease and it is enough to explain characteristics of bivariate distribution functions. Hence, it is possible to visualize trivariate empirical distribution functions with three dimensional quantile vectors. With bivariate and four variate illustrative examples, the proposed MEDFs descriptive plots are obtained and explored.

Estimating Spatial and Vertical Distribution of Seagrass Habitats Using Hydroacoustic System (수중음향을 이용한 해초 서식처(Seagrass Habitats)의 공간 및 수직 분포 추정)

  • Kang, Don-Hyung;Cho, Sung-Ho;La, Hyoung-Sul;Kim, Jong-Man;Na, Jung-Yul;Myoung, Jung-Goo
    • Ocean and Polar Research
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    • v.28 no.3
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    • pp.225-236
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    • 2006
  • Seagrass meadows are considered as critical habitats for a wide variety of marine organisms in coastal and estuarine ecosystems. In many cases, studies on the spatial/temporal distribution of seagrass have depended on direct observations using SCUBA diving. As an alternative method fur studying seagrass distribution, an application of hydroacoustic technique has been assessed for mapping seagrass distribution in Dongdae Bay, on the south coast of Korea, in September 2005. Data were collected using high frequency transducer (420 kHz split-beam), which was installed with towed body system. The system was linked to DGPS to make goo-referenced data. Additionally, in situ seagrass distribution has been observed using underwater cameras and SCUBA diving at four stations in order to compare with acoustic data. Acoustic survey was conducted along 23 transects with 3-4 blot ship speed. Seagrass beds were vertically limited to depths less than 3.5m and seagrass height ranged between 55 and 90cm at the study sites. Dense seagmss beds were mainly found at the entrance of the bay and at a flat area around the center of the bay. Although the study area was a relatively small, the vertical and spatial distributions of the seagrass were highly variable with bathymetry and region. Considering dominant species, Zostera marina L., preliminary estimation of seagrass biomass with acoustic and direct sampling data was approximately $56.55g/m^2$, and total biomass of 104 tones (coefficient variation: 25.77%) was estimated at the study area. Hydroacoustic method provided valuable information to understand distribution pattern and to estimate seagrass biomass.

GARCH Model with Conditional Return Distribution of Unbounded Johnson (Unbounded Johnson 분포를 이용한 GARCH 수익률 모형의 적용)

  • Jung, Seung-Hyun;Oh, Jung-Jun;Kim, Sung-Gon
    • The Korean Journal of Applied Statistics
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    • v.25 no.1
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    • pp.29-43
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    • 2012
  • Financial data such as stock index returns and exchange rates have the properties of heavy tail and asymmetry compared to normal distribution. When we estimate VaR using the GARCH model (with the conditional return distribution of normal) it shows the tendency of the lower estimation and clustering in the losses over the estimated VaR. In this paper, we argue that this problem can be resolved through the adaptation of the unbounded Johnson distribution as that of the condition return. We also compare this model with the GARCH with the conditional return distribution of normal and student-t. Using the losses exceed the ex-ante VaR, estimates, we check the validity of the GARCH models through the failure proportion test and the clustering test. We nd that the GARCH model with conditional return distribution of unbounded Johnson provides an appropriate estimation of the VaR and does not occur the clustering of violations.