• Journal of the Korean Data and Information Science Society
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• v.20 no.6
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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.

• Communications for Statistical Applications and Methods
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• v.14 no.3
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• pp.633-640
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• 2007

We develop noninformative priors for the ratio of the lognormal means in equal variances case. The Jeffreys' prior and reference priors are derived. We find a first order matching prior and a second order matching prior. It turns out that Jeffreys' prior and all of the reference priors are first order matching priors and in particular, one-at-a-time reference prior is a second order matching prior. One-at-a-time reference prior meets very well the target coverage probabilities. We consider the bioequivalence problem. We calculate the posterior probabilities of the hypotheses and Bayes factors under Jeffreys' prior, reference prior and matching prior using a real-life example.

• Communications for Statistical Applications and Methods
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• v.20 no.5
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• pp.387-394
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• 2013

In this paper, we develop the noninformative priors for the ratio of the scale parameters in the inverted exponential distributions. The first and second order matching priors, the reference prior and Jeffreys prior are developed. It turns out that the second order matching prior matches the alternative coverage probabilities, is a cumulative distribution function matching prior and is a highest posterior density matching prior. In addition, the reference prior and Jeffreys' prior are the second order matching prior. We show that the proposed reference prior matches the target coverage probabilities in a frequentist sense through a simulation study as well as provide an example based on real data is given.

• Journal of the Korean Data and Information Science Society
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• v.14 no.1
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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.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1521-1529
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• 2013

In this paper, we develop noninformative priors for the generalized Pareto distribution when the scale parameter is of interest. We developed the rst order and the second order matching priors. We revealed that the second order matching prior does not exist. It turns out that the reference prior and Jeffrey's prior do not satisfy a first order matching criterion, and Jeffreys' prior, the reference prior and the matching prior are different. Some simulation study is performed and a real example is given.

• Journal of The Korean Association For Science Education
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• v.27 no.9
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• pp.765-777
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• 2007

Investigations into how the status of prior learning on the textbook of science were conducted, and the effects of prior learning on students' attitudes towards science class were carried out. The investigations for the status of prior learning were performed with a number of students who experienced prior learning, (self-directed and unself-directed prior learning), a selfless intention as the starting motive for prior learning, the problem solving strategies used by students in prior learning, and the important factor that influenced prior learning. The effects of prior learning on student attitude towards science class were also examined with respect to four categories, including confidence, interest, learning intention, and value (effect). The effects of prior learning on the four categories were analyzed on the basis of the students' level of scientific achievement, the types of prior learning, the starting motive for prior learning, and the extent of the students' understanding of the content on prior learning. The analytical results for the effects of prior learning on students' attitudes towards science class showed that the mean values of confidence, learning intention, and value among the students in the self-directed prior learning group were higher than those of the students in the unself-directed prior learning group. These findings are the result of positive recognition, such as the possibility that the students experienced with self-directed prior learning can do, the induction of an inherent motive based on their level of achievement in science class, and the operant learning of strategies for solving problems in science class. Meanwhile, by the effects of having a teacher lead the science class and the consensus formed between friends, the mean level of interest was higher in the unself-directed prior learning than in the self-directed prior learning.

• Journal of the Korean Data and Information Science Society
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• v.27 no.2
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• pp.565-575
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• 2016

In this paper, we develop the noninformative priors for the linear combinations of means in the exponential distributions. We develop the matching priors and the reference priors. The matching priors, the reference prior and Jeffreys' prior for the linear combinations of means are developed. It turns out that the reference prior and Jeffreys' prior are not a matching prior. We show that the proposed matching prior matches the target coverage probabilities much more accurately than the reference prior and Jeffreys' prior in a frequentist sense through simulation study, and an example based on real data is given.

• Communications for Statistical Applications and Methods
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• v.18 no.4
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• pp.467-475
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• 2011

This paper develops the noninformative priors for the stress-strength reliability from one parameter generalized exponential distributions. When this reliability is a parameter of interest, we develop the first, second order matching priors, reference priors in its order of importance in parameters and Jeffreys' prior. We reveal that these probability matching priors are not the alternative coverage probability matching prior or a highest posterior density matching prior, a cumulative distribution function matching prior. In addition, we reveal that the one-at-a-time reference prior and Jeffreys' prior are actually a second order matching prior. We show that the proposed reference prior matches the target coverage probabilities in a frequentist sense through a simulation study and a provided example.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1241-1250
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• 2011

In this paper, we develop noninformative priors for the log-normal distributions when the parameter of interest is the common mean. We developed Jeffreys' prior, th reference priors and the first order matching priors. It turns out that the reference prior and Jeffreys' prior do not satisfy a first order matching criterion, and Jeffreys' pri the reference prior and the first order matching prior are different. Some simulation study is performed and a real example is given.

• Journal of the Korean Data and Information Science Society
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• v.25 no.1
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• pp.227-235
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• 2014

In this paper, we develop the noninformative priors for the scale parameter and the shape parameter in the log-logistic distribution. We developed the first and second order matching priors. It turns out that the second order matching prior matches the alternative coverage probabilities, and is a highest posterior density matching prior. Also we revealed that the derived reference prior is the second order matching prior for both parameters, but Jerffrey's prior is not a second order matching prior. We showed that the proposed reference prior matches the target coverage probabilities in a frequentist sense through simulation study, and an example based on real data is given.