• Title/Summary/Keyword: joint bayesian

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Copula-based common cause failure models with Bayesian inferences

  • Jin, Kyungho;Son, Kibeom;Heo, Gyunyoung
    • Nuclear Engineering and Technology
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    • v.53 no.2
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    • pp.357-367
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    • 2021
  • In general, common cause failures (CCFs) have been modeled with the assumption that components within the same group are symmetric. This assumption reduces the number of parameters required for the CCF probability estimation and allows us to use a parametric model, such as the alpha factor model. Although there are various asymmetric conditions in nuclear power plants (NPPs) to be addressed, the traditional CCF models are limited to symmetric conditions. Therefore, this paper proposes the copulabased CCF model to deal with asymmetric as well as symmetric CCFs. Once a joint distribution between the components is constructed using copulas, the proposed model is able to provide the probability of common cause basic events (CCBEs) by formulating a system of equations without symmetry assumptions. In addition, Bayesian inferences for the parameters of the marginal and copula distributions are introduced and Markov Chain Monte Carlo (MCMC) algorithms are employed to sample from the posterior distribution. Three example cases using simulated data, including asymmetry conditions in total failure probabilities and/or dependencies, are illustrated. Consequently, the copula-based CCF model provides appropriate estimates of CCFs for asymmetric conditions. This paper also discusses the limitations and notes on the proposed method.

Joint analysis of binary and continuous data using skewed logit model in developmental toxicity studies (발달 독성학에서 비대칭 로짓 모형을 사용한 이진수 자료와 연속형 자료에 대한 결합분석)

  • Kim, Yeong-hwa;Hwang, Beom Seuk
    • The Korean Journal of Applied Statistics
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    • v.33 no.2
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    • pp.123-136
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    • 2020
  • It is common to encounter correlated multiple outcomes measured on the same subject in various research fields. In developmental toxicity studies, presence of malformed pups and fetal weight are measured on the pregnant dams exposed to different levels of a toxic substance. Joint analysis of such two outcomes can result in more efficient inferences than separate models for each outcome. Most methods for joint modeling assume a normal distribution as random effects. However, in developmental toxicity studies, the response distributions may change irregularly in location and shape as the level of toxic substance changes, which may not be captured by a normal random effects model. Motivated by applications in developmental toxicity studies, we propose a Bayesian joint model for binary and continuous outcomes. In our model, we incorporate a skewed logit model for the binary outcome to allow the response distributions to have flexibly in both symmetric and asymmetric shapes on the toxic levels. We apply our proposed method to data from a developmental toxicity study of diethylhexyl phthalate.

Obtaining bootstrap data for the joint distribution of bivariate survival times

  • Kwon, Se-Hyug
    • Journal of the Korean Data and Information Science Society
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    • v.20 no.5
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    • pp.933-939
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    • 2009
  • The bivariate data in clinical research fields often has two types of failure times, which are mark variable for the first failure time and the final failure time. This paper showed how to generate bootstrap data to get Bayesian estimation for the joint distribution of bivariate survival times. The observed data was generated by Frank's family and the fake date is simulated with the Gamma prior of survival time. The bootstrap data was obtained by combining the mimic data with the observed data and the simulated fake data from the observed data.

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Parameter Estimation and Reliability Analysis Using Bayesian Approach for Bolted Joint and O-ring Seal of Solid Rocket Motor (고체 로켓 모터의 체결 볼트와 오링에 대한 베이지안 접근법 기반 모수 추정과 신뢰성 해석)

  • Gang, Jin Hyuk;Choi, Joo Ho
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.41 no.11
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    • pp.1055-1064
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    • 2017
  • Since a device such as a rocket motor requires very high reliability, a reasonable reliability design process is essential. However, Korea has implemented a design method for applying a safety factor to each component. In classic reliability analysis, input variables such as mean and standard deviation, used in the limit state function, are treated as deterministic values. Because the mean and standard deviation are determined by a small amount of data, this approach could lead to inaccurate results. In this study, reliability analysis is performed for bolted joints and o-ring seals, and the Bayesian approach is used to statistically estimate the input variables. The estimated variables and failure probability, calculated by the reliability analysis, are derived in the form of probability distributions.

Modal teat/analysis result correlation of folding fin (접는 날개에 대한 모드시험/해석결과 보정)

  • 양해석
    • Journal of KSNVE
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    • v.6 no.3
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    • pp.305-315
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    • 1996
  • Present paper aims at the correlation of modal characteristics of folding fin between test and analysis using an optimization theory. Folding fin is composed of a movable fin, a base fin, and many functional components related to the folding mechanism. Joint parts of folding fin in FEM are initially modeled as rigid elements resulting some difference between test and analysis in modal characteristics. Therefore, some equivalent springs representing joint parts are introduced to improve the FEM model. The springs were set as design variables, while the frequency difference between test and analysis was set as the object function. Bayesian procedure was ujsed for the minimization.

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A Bayesian Approach for the Analysis of Times to Multiple Events : An Application on Healthcare Data (다사건 시계열 자료 분석을 위한 베이지안 기반의 통계적 접근의 응용)

  • Seok, Junhee;Kang, Yeong Seon
    • Journal of the Korean Operations Research and Management Science Society
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    • v.39 no.4
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    • pp.51-69
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    • 2014
  • Times to multiple events (TMEs) are a major data type in large-scale business and medical data. Despite its importance, the analysis of TME data has not been well studied because of the analysis difficulty from censoring of observation. To address this difficulty, we have developed a Bayesian-based multivariate survival analysis method, which can successfully estimate the joint probability density of survival times. In this work, we extended this method for the analysis of precedence, dependency and causality among multiple events. We applied this method to the electronic health records of 2,111 patients in a children's hospital in the US and the proposed analysis successfully shows the relation between times to two types of hospital visits for different medical issues. The overall result implies the usefulness of the multivariate survival analysis method in large-scale big data in a variety of areas including marketing, human resources, and e-commerce. Lastly, we suggest our future research directions based multivariate survival analysis method.

Estimating Heterogeneous Customer Arrivals to a Large Retail store : A Bayesian Poisson model perspective (대형할인매점의 요일별 고객 방문 수 분석 및 예측 : 베이지언 포아송 모델 응용을 중심으로)

  • Kim, Bumsoo;Lee, Joonkyum
    • Korean Management Science Review
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    • v.32 no.2
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    • pp.69-78
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    • 2015
  • This paper considers a Bayesian Poisson model for multivariate count data using multiplicative rates. More specifically we compose the parameter for overall arrival rates by the product of two parameters, a common effect and an individual effect. The common effect is composed of autoregressive evolution of the parameter, which allows for analysis on seasonal effects on all multivariate time series. In addition, analysis on individual effects allows the researcher to differentiate the time series by whatevercharacterization of their choice. This type of model allows the researcher to specifically analyze two different forms of effects separately and produce a more robust result. We illustrate a simple MCMC generation combined with a Gibbs sampler step in estimating the posterior joint distribution of all parameters in the model. On the whole, the model presented in this study is an intuitive model which may handle complicated problems, and we highlight the properties and possible applications of the model with an example, analyzing real time series data involving customer arrivals to a large retail store.

A Bayesian cure rate model with dispersion induced by discrete frailty

  • Cancho, Vicente G.;Zavaleta, Katherine E.C.;Macera, Marcia A.C.;Suzuki, Adriano K.;Louzada, Francisco
    • Communications for Statistical Applications and Methods
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    • v.25 no.5
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    • pp.471-488
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    • 2018
  • In this paper, we propose extending proportional hazards frailty models to allow a discrete distribution for the frailty variable. Having zero frailty can be interpreted as being immune or cured. Thus, we develop a new survival model induced by discrete frailty with zero-inflated power series distribution, which can account for overdispersion. This proposal also allows for a realistic description of non-risk individuals, since individuals cured due to intrinsic factors (immunes) are modeled by a deterministic fraction of zero-risk while those cured due to an intervention are modeled by a random fraction. We put the proposed model in a Bayesian framework and use a Markov chain Monte Carlo algorithm for the computation of posterior distribution. A simulation study is conducted to assess the proposed model and the computation algorithm. We also discuss model selection based on pseudo-Bayes factors as well as developing case influence diagnostics for the joint posterior distribution through ${\psi}-divergence$ measures. The motivating cutaneous melanoma data is analyzed for illustration purposes.

Bayes tests of independence for contingency tables from small areas

  • Jo, Aejung;Kim, Dal Ho
    • Journal of the Korean Data and Information Science Society
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    • v.28 no.1
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    • pp.207-215
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    • 2017
  • In this paper we study pooling effects in Bayesian testing procedures of independence for contingency tables from small areas. In small area estimation setup, we typically use a hierarchical Bayesian model for borrowing strength across small areas. This techniques of borrowing strength in small area estimation is used to construct a Bayes test of independence for contingency tables from small areas. In specific, we consider the methods of direct or indirect pooling in multinomial models through Dirichlet priors. We use the Bayes factor (or equivalently the ratio of the marginal likelihoods) to construct the Bayes test, and the marginal density is obtained by integrating the joint density function over all parameters. The Bayes test is computed by performing a Monte Carlo integration based on the method proposed by Nandram and Kim (2002).

A Bayesian Approach to Linear Calibration Design Problem

  • Kim, Sung-Chul
    • Journal of the Korean Operations Research and Management Science Society
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    • v.20 no.3
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    • pp.105-122
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    • 1995
  • Based on linear models, the inference about the true measurement x$_{f}$ and the optimal designs x (nx1) for the calibration experiments are considered via Baysian statistical decision analysis. The posterior distribution of x$_{f}$ given the observation y$_{f}$ (qxl) and the calibration experiment is obtained with normal priors for x$_{f}$ and for themodel parameters (.alpha., .betha.). This posterior distribution is not in the form of any known distributions, which leads to the use of a numerical integration or an approximation for the calculation of the overall expected loss. The general structure of the expected loss function is characterized in the form of a conjecture. A near-optimal design is obtained through the approximation nof the conditional covariance matrix of the joint distribution of (x$_{f}$ , y$_{f}$ $^{T}$ )$^{T}$ . Numerical results for the univariate case are given to demonstrate the conjecture and to evaluate the approximation.n.

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