• Journal of the Korean Data and Information Science Society
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• v.22 no.4
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• pp.819-826
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• 2011

In this paper, we develop the reference priors for the scale and shape parameters in the nonregular Pareto distribution. We derive the reference priors as noninformative priors and prove the propriety of joint posterior distribution under the general priors including reference priors in the order of inferential importance. Through the simulation study, we compare the reference priors with respect to coverage probabilities of parameter of interest in a frequentist sense.

• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.977-989
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• 2011

The half logistic distribution has been used intensively in reliability and survival analysis especially when the data is censored. In this paper, we provide Bayesian estimation of the shape parameter and reliability function in the generalized half logistic distribution based on progressively Type-II censored data under various loss functions. We here consider conjugate prior and noninformative prior and corresponding posterior distributions are obtained. As an illustration, we examine the validity of our estimation using real data and simulated data.

• Journal of the Korean Data and Information Science Society
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• v.22 no.3
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• pp.565-574
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• 2011

The exponenetiated distribution has been used in reliability and survival analysis especially when the data is censored. In this paper, we derive Bayesian estimation of shape parameter and reliability function in the exponenetiated half triangle distribution based on Type-I hybrid censored data. Here we consider conjugate prior and noninformative prior and obtained corresponding posterior distributions. As an illustration, the mean square errors of the estimates are computed. Comparisons are made between these estimators using Monte Carlo simulation study.

• Communications for Statistical Applications and Methods
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• v.18 no.5
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• pp.603-613
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• 2011

Exponentiated distribution has been used in reliability and survival analysis especially when the data is censored. In this paper, we derive Bayesian estimation of the shape parameter, reliability function and failure rate function in the exponentiated distribution family based on Type-II right censored data. We here consider conjugate prior and noninformative prior and corresponding posterior distributions are obtained. As an illustration, the mean square errors of the estimates are computed. Comparisons are made between these estimators using Monte Carlo simulation study.

• Journal of the Korean Data and Information Science Society
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• v.13 no.2
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• pp.235-242
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• 2002

In this paper, we develop noninformative priors that are used for estimating the ratio of failure rates under Freund's bivariate exponential distribution. A class of priors is found by matching the coverage probabilities of one-sided Baysian credible interval with the corresponding frequentist coverage probabilities. Also the propriety of posterior under the noninformative priors is proved and the frequentist coverage probabilities are investigated for small samples via simulation study.

• The Korean Journal of Applied Statistics
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• v.29 no.2
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• pp.331-344
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• 2016

Whole Crop Barley (WCB) is a representative self-sufficient winter annual forage crop, along with Italian Ryegrass (IRG), in Korea. In this study, we examined the path relationship between WCB yield and climate factors such as temperature, precipitation, and sunshine duration using a structural equation model. A Bayesian approach was considered to overcome the limitations of the small WCB sample size. As prior distribution of parameters in Bayesian method, standard normal distribution, the posterior result of structural equation model for WCB, and the posterior result of structural equation model for IRG (which is the most popular winter crop) were used. Also, Heywood case correction in prior distribution was considered to obtain the posterior distribution of parameters; in addition, the best prior to fit the characteristics of winter crops was identified. In our analysis, we found that the best prior was set by using the results of a structural equation model to IRG with Heywood case correction. This result can provide an alternative for research on forage crops that have hard to collect sample data.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.77-86
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• 2021

A method of constructing a war simulation based on Bayesian Inference was proposed as a method of constructing heterogeneous historical war data obtained with a time difference into a single model. A method of applying a linear regression model can be considered as a method of predicting future battles by analyzing historical war results. However it is not appropriate for two heterogeneous types of historical data that reflect changes in the battlefield environment due to different times to be suitable as a single linear regression model and violation of the model's assumptions. To resolve these problems a Bayesian inference method was proposed to obtain a post-distribution by assuming the data from the previous era as a non-informative prior distribution and to infer the final posterior distribution by using it as a prior distribution to analyze the data obtained from the next era. Another advantage of the Bayesian inference method is that the results sampled by the Markov Chain Monte Carlo method can be used to infer posterior distribution or posterior predictive distribution reflecting uncertainty. In this way, it has the advantage of not only being able to utilize a variety of information rather than analyzing it with a classical linear regression model, but also continuing to update the model by reflecting additional data obtained in the future.

• Journal of the Korean Data and Information Science Society
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• v.9 no.2
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• pp.165-172
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• 1998

This paper considers a linear regression model with censored data where each error term follows a multivariate normal distribution. In this paper we consider the diffuse prior distribution for parameters of the linear regression model. With censored data we derive the full conditional densities for parameters of a multiple regression model in order to obtain the marginal posterior densities of the relevant parameters through the Gibbs Sampler, which was proposed by Geman and Geman(1984) and utilized by Gelfand and Smith(1990) with statistical viewpoint.

• Communications for Statistical Applications and Methods
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• v.24 no.5
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• pp.457-480
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• 2017

We develop a random partition procedure based on a Dirichlet process prior with Laplace distribution. Gibbs sampling of a Laplace mixture of linear mixed regressions with a Dirichlet process is implemented as a random partition model when the number of clusters is unknown. Our approach provides simultaneous partitioning and parameter estimation with the computation of classification probabilities, unlike its counterparts. A full Gibbs-sampling algorithm is developed for an efficient Markov chain Monte Carlo posterior computation. The proposed method is illustrated with simulated data and one real data of the energy efficiency of Tsanas and Xifara (Energy and Buildings, 49, 560-567, 2012).

• Journal of the Korean Data and Information Science Society
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• v.21 no.5
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• pp.935-944
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• 2010

In this paper, we develop the noninformative priors for the reliability in a stress-strength model where a strength X and a stress Y have independent exponential distributions with different scale parameters and a common location parameter. We derive the reference priors and prove the propriety of joint posterior distribution under the general prior including the reference priors. Through the simulation study, we show that the proposed reference priors match the target coverage probabilities in a frequentist sense.