• Journal of the Korean Data and Information Science Society
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• 제14권1호
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• pp.83-92
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• 2003

In this paper, we derive noninformative priors for the ratio of parameters in the Burr model. We obtain Jeffreys' prior, reference prior and second order probability matching prior. Also we prove that the noninformative prior matches the alternative coverage probabilities and a HPD matching prior up to the second order, respectively. Finally, we provide simulated frequentist coverage probabilities under the derived noninformative priors for small and moderate size of samples.

• 한국산업정보학회:학술대회논문집
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• 한국산업정보학회 2009년도 춘계학술대회 미래 IT융합기술 및 전략
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• pp.107-113
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• 2009

This paper deals with noninformative priors for such as Jeffres' prior, reference prior and probability matching prior for scale parameter of exponential distribution when the data are collected in multiple step stress accelerated life tests. We find the noninformative priors for this model and show that the reference prior satisfies first order matching criterion. Using artificial data, we perform Bayesian analysis for proposed priors.

• Communications for Statistical Applications and Methods
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• 제10권3호
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• pp.719-729
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• 2003

We propose a Bayesian model selection procedure for nonlinear regression models under noninformative prior. For informative prior, Na and Kim (2002) suggested the Bayesian model selection procedure through MCMC techniques. We extend this method to the case of noninformative prior. The difficulty with the use of noninformative prior is that it is typically improper and hence is defined only up to arbitrary constant. The methods, such as Intrinsic Bayes Factor(IBF) and Fractional Bayes Factor(FBF), are used as a resolution to the problem. We showed the detailed model selection procedure through the specific real data set.

• Journal of the Korean Data and Information Science Society
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• 제16권1호
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• pp.71-78
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• 2005

Recently an important issue in Bayesian methodology is determination of noninformative prior distributions, often required when there is no idea of prior information. In this thesis attention is focused on the development of noninformative priors for stationary AR(1) model. The noninformative priors primarily discussed are the Jeffreys prior, and the reference priors. The remarkable points in the result are that the Jeffreys prior coincides with the reference prior for the case that $\rho$ is the parameter of interest.

• Communications for Statistical Applications and Methods
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• 제12권3호
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• pp.729-742
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• 2005

In this paper, we discuss the propriety of the various noninformative priors for the Pareto distribution. The reference prior, Jeffreys prior and ad hoc noninformative prior which is used in several literatures will be introduced and showed that which prior gives the proper posterior distribution. The reference prior and Jeffreys prior give a proper posterior distribution, but ad hoc noninformative prior which is proportional to reciprocal of the parameters does not give a proper posterior. To compute survival function, we use the well-known approximation method proposed by Lindley (1980) and Tireney and Kadane (1986). And two methods are compared by simulation. A real data example is given to illustrate our methodology.

• International Journal of Reliability and Applications
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• 제2권2호
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• pp.117-130
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• 2001

Consider the problem of estimating the system reliability using noninformative priors when both stress and strength follow generalized gamma distributions. We first treat the orthogonal reparametrization and then, using this reparametrization, derive Jeffreys'prior, reference prior, and matching priors. We next provide the suffcient condition for propriety of posterior distributions under those noninformative priors. Finally, we provide and compare estimated values of the system reliability based on the simulated values of the parameter of interest in some special cases.

• Journal of the Korean Data and Information Science Society
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• 제13권2호
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• pp.217-226
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• 2002

In this paper, we derive noninformative priors for the ratio of failure rates in exponential model. A class of priors is found by matching the coverage probabilities of one-sided Baysian credible interval with the corresponding frequentist coverage probabilities. And we prove that the noninformative prior matches the alternative coverage probabilities and is a HPD matching prior up to the second order. Finally, we provide simulated freqentist coverage probabilities under the derived noninformative prior for small samples.

• Journal of the Korean Statistical Society
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• 제36권1호
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• pp.77-91
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• 2007

This paper considers development of noninformative priors for the familial data when the families have equal number of offspring. Several noninformative priors including the widely used Jeffreys' prior as well as the different reference priors are derived. Also, a simultaneously-marginally-probability-matching prior is considered and probability matching priors are derived when the parameter of interest is inter- or intra-class correlation coefficient. The simulation study implemented by Gibbs sampler shows that two-group reference prior is slightly edge over the others in terms of coverage probability.

• Communications for Statistical Applications and Methods
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• 제9권2호
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• pp.401-410
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• 2002

This paper revisits parallel-line bioassay problem, from a Bayesian point of view using noninformative priors such as Jeffreys' prior, reference priors, and probability matching priors. After finding the orthogonal transformation, the class of first order and second order probability matching priors are derived. Jeffreys' prior and reference priors are derived also. Numerical examples are given to show the effectiveness of noninformative priors.

• Journal of the Korean Data and Information Science Society
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• 제20권6호
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• pp.1213-1223
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• 2009

In this paper, we develop noninformative priors for two parameter Pareto distribution. Specially, we derive Jereys' prior, probability matching prior and reference prior for the parameter of interest. In our case, the probability matching prior is only a first order matching prior and there does not exist a second order matching prior. Some simulation reveals that the matching prior performs better to achieve the coverage probability. A real example is also considered.