• Title/Summary/Keyword: data distributions

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Default Bayesian hypothesis testing for the scale parameters in nonregular Pareto distributions

  • Kang, Sang Gil
    • Journal of the Korean Data and Information Science Society
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    • v.23 no.6
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    • pp.1299-1308
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    • 2012
  • This article deals with the problem of testing the equality of the scale parameters in nonregular Pareto distributions.We propose Bayesian hypothesis testing procedures for the equality of the scale parameters under the noninformative prior. The noninformative prior is usually improper which yields a calibration problem that makes the Bayes factor to be de ned up to a multiplicative constant. So we propose the default Bayesia hypothesis testing procedures based on the fractional Bayes factor and the intrinsic Bayes factors under the reference priors. Simulation study and a real data example are provided.

Default Bayesian testing equality of scale parameters in several inverse Gaussian distributions

  • Kang, Sang Gil;Kim, Dal Ho;Lee, Woo Dong
    • Journal of the Korean Data and Information Science Society
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    • v.26 no.3
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    • pp.739-748
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    • 2015
  • This paper deals with the problem of testing about the equality of the scale parameters in several inverse Gaussian distributions. We propose default Bayesian testing procedures for the equality of the shape parameters under the reference priors. The reference prior is usually improper which yields a calibration problem that makes the Bayes factor to be defined up to a multiplicative constant. Therefore we propose the default Bayesian testing procedures based on the fractional Bayes factor and the intrinsic Bayes factors under the reference priors. Simulation study and an example are provided.

The Bivariate Kumaraswamy Weibull regression model: a complete classical and Bayesian analysis

  • Fachini-Gomes, Juliana B.;Ortega, Edwin M.M.;Cordeiro, Gauss M.;Suzuki, Adriano K.
    • Communications for Statistical Applications and Methods
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    • v.25 no.5
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    • pp.523-544
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    • 2018
  • Bivariate distributions play a fundamental role in survival and reliability studies. We consider a regression model for bivariate survival times under right-censored based on the bivariate Kumaraswamy Weibull (Cordeiro et al., Journal of the Franklin Institute, 347, 1399-1429, 2010) distribution to model the dependence of bivariate survival data. We describe some structural properties of the marginal distributions. The method of maximum likelihood and a Bayesian procedure are adopted to estimate the model parameters. We use diagnostic measures based on the local influence and Bayesian case influence diagnostics to detect influential observations in the new model. We also show that the estimates in the bivariate Kumaraswamy Weibull regression model are robust to deal with the presence of outliers in the data. In addition, we use some measures of goodness-of-fit to evaluate the bivariate Kumaraswamy Weibull regression model. The methodology is illustrated by means of a real lifetime data set for kidney patients.

Three-Parameter Gamma Distribution and Its Significance in Structural Reliability

  • Zhao, Yan-Gang;Alfredo H-S. Ang
    • Computational Structural Engineering : An International Journal
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    • v.2 no.1
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    • pp.1-10
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    • 2002
  • Information on the distribution of the basic random variables is essential for the accurate evaluation of structural reliability. The usual method for determining the distribution is to fit a candidate distribution to the histogram of available statistical data of the variable and perform appropriate goodness-of-fit tests. Generally, such candidate distributions would have two parameters that may be evaluated from the mean value and standard deviation of the statistical data. In the present paper, a-parameter Gamma distribution, whose parameters can be directly defined in terms of the mean value, standard deviation and skewness of available data, is suggested. The flexibility and advantages of the distribution in fitting statistical data and its significance in structural reliability evaluation are identified and discussed. Numerical examples are presented to demonstrate these advantages.

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Noninformative priors for the ratio of the scale parameters in the half logistic distributions

  • Kang, Sang-Gil;Kim, Dal-Ho;Lee, Woo-Dong
    • Journal of the Korean Data and Information Science Society
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    • v.23 no.4
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    • pp.833-841
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    • 2012
  • In this paper, we develop the noninformative priors for the ratio of the scale parameters in the half logistic distributions. We develop the first and second order matching priors. It turns out that the second order matching prior matches the alternative coverage probabilities, and is a highest posterior density matching prior. Also we reveal that the one-at-a-time reference prior and Jeffreys' prior are the second order matching prior. We show that the proposed reference prior matches the target coverage probabilities in a frequentist sense through simulation study, and an example based on real data is given.

Hidden truncation circular normal distribution

  • Kim, Sung-Su;Sengupta, Ashis
    • Journal of the Korean Data and Information Science Society
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    • v.23 no.4
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    • pp.797-805
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    • 2012
  • Many circular distributions are known to be not only asymmetric but also bimodal. Hidden truncation method of generating asymmetric distribution is applied to a bivariate circular distribution to generate an asymmetric circular distribution. While many other existing asymmetric circular distributions can only model an asymmetric data, this new circular model has great flexibility in terms of asymmetry and bi-modality. Some properties of the new model, such as the trigonometric moment generating function, and asymptotic inference about the truncation parameter are presented. Simulation and real data examples are provided at the end to demonstrate the utility of the novel distribution.

Bayesian Hypothesis Testing for the Ratio of Two Quantiles in Exponential Distributions

  • Kang, Sang-Gil;Kim, Dal-Ho;Lee, Woo-Dong
    • Journal of the Korean Data and Information Science Society
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    • v.18 no.3
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    • pp.833-845
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    • 2007
  • When X and Y have independent exponential distributions, we develop a Bayesian testing procedure for the ratio of two quantiles under reference prior. The noninformative prior such as reference prior is usually improper which yields a calibration problem that makes the Bayes factor to be defined up to a multiplicative constant. So we develop a Bayesian testing procedure based on fractional Bayes factor and intrinsic Bayes factor. We show that the posterior density under the reference prior is proper and propose the Bayesian testing procedure for the ratio of two quantiles using fractional Bayes factor and intrinsic Bayes factor. Simulation study and a real data example are provided.

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207 NEW OPEN STAR CLUSTERS WITHIN 1 KPC FROM GAIA DATA RELEASE 2

  • Sim, Gyuheon;Lee, Sang Hyun;Ann, Hong Bae;Kim, Seunghyeon
    • Journal of The Korean Astronomical Society
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    • v.52 no.5
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    • pp.145-158
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    • 2019
  • We conducted a survey of open clusters within 1 kpc from the Sun using the astrometric and photometric data of the Gaia Data Release 2. We found 655 cluster candidates by visual inspection of the stellar distributions in proper motion space and spatial distributions in l - b space. All of the 655 cluster candidates have a well defined main-sequence except for two candidates if we consider that the main sequence of very young clusters is somewhat broad due to differential extinction. Cross-matching of our 653 open clusters with known open clusters in various catalogs resulted in 207 new open clusters. We present the physical properties of the newly discovered open clusters. The majority of the newly discovered open clusters are of young to intermediate age and have less than ~50 member stars.

Numerical Study on the Impact of SST Spacial Distribution on Regional Circulation (상세 해수면 온도자료의 반영에 따른 국지 기상정 개선에 관한 수치연구)

  • Jeon, Won-Bae;Lee, Hwa-Woon;Lee, Soon-Hwan;Choi, Hyun-Jung;Leem, Heon-Ho
    • Journal of Korean Society for Atmospheric Environment
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    • v.25 no.4
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    • pp.304-315
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    • 2009
  • Numerical simulations were carried out to understand the effect of Sea Surface Temperature (SST) spatial distribution on regional circulation. A three-dimensional non-hydrostatic atmospheric model RAMS, version 6.0, was applied to examine the impact of SST forcing on regional circulation. New Generation Sea Surface Temperature (NGSST) data were implemented to RAMS to compare the results of modeling with default SST data. Several numerical experiments have been undertaken to evaluate the effect of SST for initialization. First was the case with NGSST data (Case NG), second was the case with RAMS monthly data (Case RM) and third was the case with seasonally averaged RAMS monthly data (Case RS). Case NG showed accurate spatial distributions of SST but, the results of RM and RS were $3{\sim}4^{\circ}C$ lower than buoy observation data. By analyzing practical sea surface conditions, large difference in horizontal temperature and wind field for each run were revealed. Case RM and Case RS showed similar horizontal and vertical distributions of temperature and wind field but, Case NG estimated the intensity of sea breeze weakly and land breeze strongly. These differences were due to the difference of the temperature gradient caused by different spatial distributions of SST. Diurnal variations of temperature and wind speed for Case NG indicated great agreement with the observation data and statistics such as root mean squared error, index of agreement, regression were also better than Case RM and Case RS.

Spatiotemporal Data Visualization using Gravity Model (중력 모델을 이용한 시공간 데이터의 시각화)

  • Kim, Seokyeon;Yeon, Hanbyul;Jang, Yun
    • Journal of KIISE
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    • v.43 no.2
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    • pp.135-142
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    • 2016
  • Visual analysis of spatiotemporal data has focused on a variety of techniques for analyzing and exploring the data. The goal of these techniques is to explore the spatiotemporal data using time information, discover patterns in the data, and analyze spatiotemporal data. The overall trend flow patterns help users analyze geo-referenced temporal events. However, it is difficult to extract and visualize overall trend flow patterns using data that has no trajectory information for movements. In order to visualize overall trend flow patterns, in this paper, we estimate continuous distributions of discrete events over time using KDE, and we extract vector fields from the continuous distributions using the gravity model. We then apply our technique on twitter data to validate techniques.