• Title/Summary/Keyword: data distributions

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Binary classification on compositional data

  • Joo, Jae Yun;Lee, Seokho
    • Communications for Statistical Applications and Methods
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    • v.28 no.1
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    • pp.89-97
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    • 2021
  • Due to boundedness and sum constraint, compositional data are often transformed by logratio transformation and their transformed data are put into traditional binary classification or discriminant analysis. However, it may be problematic to directly apply traditional multivariate approaches to the transformed data because class distributions are not Gaussian and Bayes decision boundary are not polynomial on the transformed space. In this study, we propose to use flexible classification approaches to transformed data for compositional data classification. Empirical studies using synthetic and real examples demonstrate that flexible approaches outperform traditional multivariate classification or discriminant analysis.

Effect and uncertainty analysis according to input components and their applicable probability distributions of the Modified Surface Water Supply Index (Modified Surface Water Supply Index의 입력인자와 적용 확률분포에 따른 영향과 불확실성 분석)

  • Jang, Suk Hwan;Lee, Jae-Kyoung;Oh, Ji Hwan;Jo, Joon Won
    • Journal of Korea Water Resources Association
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    • v.50 no.7
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    • pp.475-488
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    • 2017
  • To simulate accurate drought, a drought index is needed to reflect the hydrometeorological phenomenon. Several studies have been conducted in Korea using the Modified Surface Water Supply Index (MSWSI) to simulate hydrological drought. This study analyzed the limitations of MSWSI and quantified the uncertainties of MSWSI. The influence of hydrometeorological components selected as the MSWSI components was analyzed. Although the previous MSWSI dealt with only one observation for each input component such as streamflow, ground water level, precipitation, and dam inflow, this study included dam storage level and dam release as suitable characteristics of the sub-basins, and used the areal-average precipitation obtained from several observations. From the MSWSI simulations of 2001 and 2006 drought events, MSWSI of this study successfully simulated drought because MSWSI of this study followed the trend of observing the hydrometeorological data and then the accuracy of the drought simulation results was affected by the selection of the input component on the MSWSI. The influence of the selection of the probability distributions to input components on the MSWSI was analyzed, including various criteria: the Gumbel and Generalized Extreme Value (GEV) distributions for precipitation data; normal and Gumbel distributions for streamflow data; 2-parameter log-normal and Gumbel distributions for dam inflow, storage level, and release discharge data; and 3-parameter log-normal distribution for groundwater. Then, the maximum 36 MSWSIs were calculated for each sub-basin, and the ranges of MSWSI differed significantly according to the selection of probability distributions. Therefore, it was confirmed that the MSWSI results may differ depending on the probability distribution. The uncertainty occurred due to the selection of MSWSI input components and the probability distributions were quantified using the maximum entropy. The uncertainty thus increased as the number of input components increased and the uncertainty of MSWSI also increased with the application of probability distributions of input components during the flood season.

Evaluation of electron dose distribution obtained from ADAC Pinnacle system against measurement and Monte Carlo method for breast patients

  • Lee, S.;Lee, R.;Park, D.;S. Suh
    • Proceedings of the Korean Society of Medical Physics Conference
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    • 2003.09a
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    • pp.82-82
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    • 2003
  • Introduction: With the development of dose calculation algorithms for electron beams, 3D RTP systerns are available for electron beam dose distribution commercially. However, no studies evaluated the accuracy of dose calculation with ADAC Pinnacle system for electron beams. So, the accuracy of the ADAC system is investigated by comparing electron dose distributions from ADAC system against the BEAMnrc/DOSXYZnrc. Methods: A total of 33 breast cancer patients treated with 6, 9, and 12MeV electrons in our institution was selected for this study. The first part of this study is to compare the dose distributions of measurement, TPS and the BEAMnrc/DOSXYZnrc code in flat water phantom at gantry zero position and for a 10 ${\times}$ 10 $\textrm{cm}^2$ field. The second part is to evaluate the monitor unit obtained from measurement and TPS. Adding actual breast patient's irregular blocks to the first part, monitor units to deliver 100 cGy to the dose maximum (dmax) were calculated from measurement and 3D RTP system. In addition, the dose distributions using blocks were compared between TPS and the BEAMnrc/DOSXYZnrc code. Finally, the effects of tissue inhomogeneities were studied by comparing dose distributions from Pinnacle and Monte Carlo method on CT data sets. Results: The dose distributions calculated using water phantom by the TPS and the BEAMnrc/ DOSXYZnrc code agreed well with measured data within 2% of the maximum dose. The maximum differences of monitor unit between measured and Pinnacle TPS in flat water phantom at gantry zero position were 4% for 6 MeV and 2% for 9 and 12 MeV electrons. In real-patient cases, comparison of depth doses and lateral dose profiles calculated by the Pinnacle TPS, with BEAMnrc/DOSXYZnrc code has generally shown good agreement with relative difference less than +/-3%. Discussion: For comparisons of real-patient cases, the maximum differences between the TPS and BEAMnrc/DOSXYZnrc on CT data were 10%. These discrepancies were due in part to the inaccurate dose calculation of the TPS, so that it needs to be improved properly. Conclusions: On the basis of the results presented in this study, we can conclude that the ADAC Pinnacle system for electron beams is capable of giving results absolutely comparable to those of a Monte Carlo calculation.

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Quasi-Likelihood Estimation for ARCH Models

  • Kim, Sah-Myeong
    • Journal of the Korean Data and Information Science Society
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    • v.16 no.3
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    • pp.651-656
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    • 2005
  • In this paper the quasi-likelihood function was proposed and the estimators which are the solutions of the estimating equations for estimation of a class of nonlinear time series models. We compare the performances of the proposed estimators with those of the ML estimators under the heavy-railed distributions by simulation.

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The UMVUE and MLE of the Tail Probability in Discrete Model

  • Woo, Jung-Soo
    • Journal of the Korean Data and Information Science Society
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    • v.17 no.4
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    • pp.1405-1412
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    • 2006
  • We shall derive the UMVUE of the tail probability in Poisson, Binomial, and negative Binomial distributions, and compare means squared errors of the UMVUE and the MLE of the tail probability in each case.

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Recurrence Relations in the Transformed Exponential Distributions

  • Choi, Jeen-Kap;Mo, Kap-Jong
    • Journal of the Korean Data and Information Science Society
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    • v.14 no.4
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    • pp.1031-1044
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    • 2003
  • In this paper, we establish some recurrence relations of the moments, product moments, percentage points, and modes of order statistics from the transformed exponential distribution.

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Domestic earthquake prediction using bayesian approach (베이지안 기법을 이용한 국내 지진 사고 예측)

  • Yang, Hee-Joong
    • Journal of the Korea Safety Management & Science
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    • v.11 no.4
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    • pp.119-125
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    • 2009
  • We predict the earthquake rate in Korea following Bayesian approach. We make a model that can utilize the data to predict other levels of earthquake. An event tree model which is a frequently used graphical tool in describing accident initiation and escalation to more severe accident is transformed into an influence diagram model. Prior distributions for earthquake occurrence rate and probabilities to escalating to more severe earthquakes are assumed and likelihood of number of earthquake in a given period of time is assessed. And then posterior distributions are obtained based on observed data. We find that the minor level of earthquake is increasing while major level of earthquake is less likely.

BAYESIAN ROBUST ANALYSIS FOR NON-NORMAL DATA BASED ON A PERTURBED-t MODEL

  • Kim, Hea-Jung
    • Journal of the Korean Statistical Society
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    • v.35 no.4
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    • pp.419-439
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    • 2006
  • The article develops a new class of distributions by introducing a nonnegative perturbing function to $t_\nu$ distribution having location and scale parameters. The class is obtained by using transformations and conditioning. The class strictly includes $t_\nu$ and $skew-t_\nu$ distributions. It provides yet other models useful for selection modeling and robustness analysis. Analytic forms of the densities are obtained and distributional properties are studied. These developments are followed by an easy method for estimating the distribution by using Markov chain Monte Carlo. It is shown that the method is straightforward to specify distribution ally and to implement computationally, with output readily adopted for constructing required criterion. The method is illustrated by using a simulation study.

Transmuted new generalized Weibull distribution for lifetime modeling

  • Khan, Muhammad Shuaib;King, Robert;Hudson, Irene Lena
    • Communications for Statistical Applications and Methods
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    • v.23 no.5
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    • pp.363-383
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    • 2016
  • The Weibull family of lifetime distributions play a fundamental role in reliability engineering and life testing problems. This paper investigates the potential usefulness of transmuted new generalized Weibull (TNGW) distribution for modeling lifetime data. This distribution is an important competitive model that contains twenty-three lifetime distributions as special cases. We can obtain the TNGW distribution using the quadratic rank transmutation map (QRTM) technique. We derive the analytical shapes of the density and hazard functions for graphical illustrations. In addition, we explore some mathematical properties of the TNGW model including expressions for the quantile function, moments, entropies, mean deviation, Bonferroni and Lorenz curves and the moments of order statistics. The method of maximum likelihood is used to estimate the model parameters. Finally the applicability of the TNGW model is presented using nicotine in cigarettes data for illustration.