• Title/Summary/Keyword: sampler model

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Bayesian small area estimations with measurement errors

  • Goo, You Mee;Kim, Dal Ho
    • Journal of the Korean Data and Information Science Society
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    • v.24 no.4
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    • pp.885-893
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    • 2013
  • This paper considers Bayes estimations of the small area means under Fay-Herriot model with measurement errors. We provide empirical Bayes predictors of small area means with the corresponding jackknifed mean squared prediction errors. Also we obtain hierarchical Bayes predictors and the corresponding posterior standard deviations using Gibbs sampling. Numerical studies are provided to illustrate our methods and compare their eciencies.

Bayes Computations for the Reliability in a Bivariate Exponential Model

  • In Suk Lee;Jang Sik Cho;Sang Gil Kang;Jeong Hwan Ko
    • Communications for Statistical Applications and Methods
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    • v.5 no.1
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    • pp.145-153
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    • 1998
  • In this paper, a hierarchical Bayesian analysis of a bivariate exponential model is discussed using Gibbs sampler. Parameters and reliability estimators are obtained. A numerical study is provided.

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Hydrograph Separation using Geochemical tracers by Three-Component Mixing Model for the Coniferous Forested Catchment in Gwangneung Gyeonggido, Republic of Korea

  • Kim, Kyongha;Yoo, Jae-Yun
    • Journal of Korean Society of Forest Science
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    • v.96 no.5
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    • pp.561-566
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    • 2007
  • This study was conducted to clarify runoff production processes in forested catchment through hydrograph separation using three-component mixing model based on the End Member Mixing Analysis (EMMA) model. The study area is located in the coniferous-forested experimental catchment, Gwangneung Gyeonggido near Seoul, Korea (N 37 45', E 127 09'). This catchment is covered by Pinus Korainensis and Abies holophylla planted at stocking rate of 3,000 trees $ha^{-1}$ in 1976. Thinning and pruning were carried out two times in the spring of 1996 and 2004 respectively. We monitored 8 successive events during the periods from June 15 to September 15, 2005. Throughfall, soil water and groundwater were sampled by the bulk sampler. Stream water was sampled every 2-hour through ISCO automatic sampler for 48 hours. The geochemical tracers were determined in the result of principal components analysis. The concentrations of $SO_4{^{2-}$ and $Na^+$ for stream water almost were distributed within the bivariate plot of the end members; throughfall, soil water and groundwater. Average contributions of throughfall, soil water and groundwater on producing stream flow for 8 events were 17%, 25% and 58% respectively. The amount of antecedent precipitation (AAP) plays an important role in determining which end members prevail during the event. It was found that ground water contributed more to produce storm runoff in the event of a small AAP compared with the event of a large AAP. On the other hand, rain water showed opposite tendency to ground water. Rain water in storm runoff may be produced by saturation overland flow occurring in the areas where soil moisture content is near saturation. AAP controls the producing mechanism for storm runoff whether surface or subsurface flow prevails.

A Bayesian Approach to Detecting Outliers Using Variance-Inflation Model

  • Lee, Sangjeen;Chung, Younshik
    • Communications for Statistical Applications and Methods
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    • v.8 no.3
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    • pp.805-814
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    • 2001
  • The problem of 'outliers', observations which look suspicious in some way, has long been one of the most concern in the statistical structure to experimenters and data analysts. We propose a model for outliers problem and also analyze it in linear regression model using a Bayesian approach with the variance-inflation model. We will use Geweke's(1996) ideas which is based on the data augmentation method for detecting outliers in linear regression model. The advantage of the proposed method is to find a subset of data which is most suspicious in the given model by the posterior probability The sampling based approach can be used to allow the complicated Bayesian computation. Finally, our proposed methodology is applied to a simulated and a real data.

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A Bayesian Threshold Model for Ordered Categorical Traits (순서범주형자료 분석을 위한 베이지안 분계점 모형)

  • Choi Byangsu;Lee Seung-Chun
    • The Korean Journal of Applied Statistics
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    • v.18 no.1
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    • pp.173-182
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    • 2005
  • A Bayesian threshold model is considered to analyze binary or ordered categorical traits. Gibbs sampler for making full Bayesian inferences about the category probability as well as the regression coefficients is described. The model can be regarded as an alternative to the ordered logit regression model. Numerical examples are shown to demonstrate the efficiency of the model.

Bayesian Change-point Model for ARCH

  • Nam, Seung-Min;Kim, Ju-Won;Cho, Sin-Sup
    • Communications for Statistical Applications and Methods
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    • v.13 no.3
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    • pp.491-501
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    • 2006
  • We consider a multiple change point model with autoregressive conditional heteroscedasticity (ARCH). The model assumes that all or the part of the parameters in the ARCH equation change over time. The occurrence of the change points is modelled as the discrete time Markov process with unknown transition probabilities. The model is estimated by Markov chain Monte Carlo methods based on the approach of Chib (1998). Simulation is performed using a variant of perfect sampling algorithm to achieve the accuracy and efficiency. We apply the proposed model to the simulated data for verifying the usefulness of the model.

Nonstationary Frequency Analysis of Hydrologic Extreme Variables Considering of Seasonality and Trend (계절성과 경향성을 고려한 극치수문자료의 비정상성 빈도해석)

  • Lee, Jeong-Ju;Kwon, Hyun-Han;Moon, Young-Il
    • Proceedings of the Korea Water Resources Association Conference
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    • 2010.05a
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    • pp.581-585
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    • 2010
  • This study introduced a Bayesian based frequency analysis in which the statistical trend seasonal analysis for hydrologic extreme series is incorporated. The proposed model employed Gumbel and GEV extreme distribution to characterize extreme events and a fully coupled bayesian frequency model was finally utilized to estimate design rainfalls in Seoul. Posterior distributions of the model parameters in both trend and seasonal analysis were updated through Markov Chain Monte Carlo Simulation mainly utilizing Gibbs sampler. This study proposed a way to make use of nonstationary frequency model for dynamic risk analysis, and showed an increase of hydrologic risk with time varying probability density functions. In addition, full annual cycle of the design rainfall through seasonal model could be applied to annual control such as dam operation, flood control, irrigation water management, and so on. The proposed study showed advantage in assessing statistical significance of parameters associated with trend analysis through statistical inference utilizing derived posterior distributions.

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Emission Characteristics of Elemental Constituents in Fine Particulate Matter Using a Semi-continuous Measurement System (준 실시간 측정시스템을 이용한 미세입자 원소성분 배출특성 조사)

  • Park, Seung-Shik;Ondov, John M.
    • Journal of Korean Society for Atmospheric Environment
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    • v.26 no.2
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    • pp.190-201
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    • 2010
  • Fine particulate matter < $1.8{\mu}m$ was collected as a slurry using the Semicontinuous Elements in Aerosol Sampler with time resolution of 30-min between May 23 and 27, 2002 at the Sydney Supersite, Florida, USA. Concentrations of 11 elements, i.e., Al, As, Cd, Cr, Cu, Fe, Mn, Ni, Pb, Se, and Zn, in the collected slurry samples were determined off-line by simultaneous multi-element graphite furnace atomic absorption spectrometry. Temporal profiles of $SO_2$ and elemental concentrations combined with meteorological parameters such as wind direction and wind speed indicate that some transient events in their concentrations are highly correlated with the periods when the plume from an animal feed supplement processing facility influenced the Sydney sampling site. The peaking concentrations of the elemental species during the transient events varied clearly as the plume intensity varied, but the relative concentrations for As, Cr, Pb, and Zn with respect to Cd showed almost consistent values. During the transient events, metal concentrations increased by factors of >10~100 due to the influence of consistent plumes from an individual stationary source. Also the multi-variate air dispersion receptor model, which was previously developed by Park et al. (2005), was applied to ambient $SO_2$ and 8 elements (Al, As, Cd, Cr, Cu, Fe, Pb, and Zn) measurements between 20:00 May 23 and 09:30 May 24 when winds blew from between 70 and $85^{\circ}$, in which animal feed processing plant is situated, to determine emission and ambient source contributions rates of $SO_2$ and elements from one animal feed processing plant. Agreement between observed and predicted $SO_2$ concentrations was excellent (R of 0.99; and their ratio, $1.09{\pm}0.35$) when one emission source was used in the model. Average ratios of observed and predicted concentrations for As, Cd, Cr, Pb, and Zn varied from $0.83{\pm}0.26$ for Pb to $1.12{\pm}0.53$ for Cd.

Importance Sampling Embedded Experimental Frame Design for Efficient Monte Carlo Simulation (효율적인 몬테 칼로 시뮬레이션을 위한 중요 샘플링 기법이 내장된 실험 틀 설계)

  • Seo, Kyung-Min;Song, Hae-Sang
    • The Journal of the Korea Contents Association
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    • v.13 no.4
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    • pp.53-63
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    • 2013
  • This paper presents an importance sampling(IS) embedded experimental frame(EF) design for efficient Monte Carlo (MC) simulation. To achieve IS principles, the proposed EF contains two embedded sub-models, which are classified into Importance Sampler(IS) and Bias Compensator(BC) models. The IS and BC models stand between the existing system model and EF, which leads to enhancement of model reusability. Furthermore, the proposed EF enables to achieve fast stochastic simulation as compared with the crude MC technique. From the abstract two case studies with the utilization of the proposed EF, we can gain interesting experimental results regarding remarkable enhancement of simulation performance. Finally, we expect that this work will serve various content areas for enhancing simulation performance, and besides, it will be utilized as a tool to understand and analyze social phenomena.

A BAYESIAN APPROACH FOR A DECOMPOSITION MODEL OF SOFTWARE RELIABILITY GROWTH USING A RECORD VALUE STATISTICS

  • Choi, Ki-Heon;Kim, Hee-Cheul
    • Journal of applied mathematics & informatics
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    • v.8 no.1
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    • pp.243-252
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    • 2001
  • The points of failure of a decomposition process are defined to be the union of the points of failure from two component point processes for software reliability systems. Because sampling from the likelihood function of the decomposition model is difficulty, Gibbs Sampler can be applied in a straightforward manner. A Markov Chain Monte Carlo method with data augmentation is developed to compute the features of the posterior distribution. For model determination, we explored the prequential conditional predictive ordinate criterion that selects the best model with the largest posterior likelihood among models using all possible subsets of the component intensity functions. A numerical example with a simulated data set is given.