• The Korean Journal of Applied Statistics
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• v.27 no.6
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• pp.1029-1038
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• 2014

Bayesian experimental design is a useful concept in applied statistics for the design of efficient experiments especially if prior knowledge in the experiment is available. However, a theoretical or numerical approach is not simple to implement. We review the concept of a Bayesian experiment approach for linear and nonlinear statistical models. We investigate relationships between prior knowledge and optimal design to identify Bayesian experimental design process characteristics. A balanced design is important if we do not have prior knowledge; however, prior knowledge is important in design and expert opinions should reflect an efficient analysis. Care should be taken if we set a small sample size with a vague improper prior since both Bayesian design and non-Bayesian design provide incorrect solutions.

• Journal of the Korea Concrete Institute
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• v.22 no.2
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• pp.171-177
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• 2010

Recently, Performance Based Design (PBD) method has been studied as a next generation structural design method, which enables a designed structure to satisfy the required performance during its service life. One method of deciding whether the required performance has been satisfied is Bayesian method, which has been commonly used in seismic analysis. Generally, it is presented as a conditional probability of exceeding some limit state (i.e., collapse) for a given ground motion. In PBD of concrete mixture design, the same methodology can be applied to assess concrete material performance based on some conditional parameters (i.e. strength, workability, carbonation, etc). In this paper, a detailed explanation of the procedure of drawing satisfaction curve by using Bayesian method based on various material parameters is shown. Also, a discussion of using the developed satisfaction curves for PBD for concrete mixture design is presented.

• Communications for Statistical Applications and Methods
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• v.24 no.6
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• pp.561-581
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• 2017

Bayesian statistics can play a key role in the design and analysis of clinical trials and this has been demonstrated for medical device trials. By 1995 Bayesian statistics had been well developed and the revolution in computing powers and Markov chain Monte Carlo development made calculation of posterior distributions within computational reach. The Food and Drug Administration (FDA) initiative of Bayesian statistics in medical device clinical trials, which began almost 20 years ago, is reviewed in detail along with some of the key decisions that were made along the way. Both Bayesian hierarchical modeling using data from previous studies and Bayesian adaptive designs, usually with a non-informative prior, are discussed. The leveraging of prior study data has been accomplished through Bayesian hierarchical modeling. An enormous advantage of Bayesian adaptive designs is achieved when it is accompanied by modeling of the primary endpoint to produce the predictive posterior distribution. Simulations are crucial to providing the operating characteristics of the Bayesian design, especially for a complex adaptive design. The 2010 FDA Bayesian guidance for medical device trials addressed both approaches as well as exchangeability, Type I error, and sample size. Treatment response adaptive randomization using the famous extracorporeal membrane oxygenation example is discussed. An interesting real example of a Bayesian analysis using a failed trial with an interesting subgroup as prior information is presented. The implications of the likelihood principle are considered. A recent exciting area using Bayesian hierarchical modeling has been the pediatric extrapolation using adult data in clinical trials. Historical control information from previous trials is an underused area that lends itself easily to Bayesian methods. The future including recent trends, decision theoretic trials, Bayesian benefit-risk, virtual patients, and the appalling lack of penetration of Bayesian clinical trials in the medical literature are discussed.

• Journal of Korean Society for Quality Management
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• v.22 no.1
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• pp.96-112
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• 1994

This article studies the design of Bayesian single attribute acceptance sampling plans under dependent production processes. An economic model is constructed by extending the mathematical model developed for non-Bayesian cases for Bayesian cases. The mathematical structure of the model is analyzed and it is used to prove that optimization of the model can be achieved by applying the solution method developed for non-Bayesian models directly. The effect of dependence patterns and the types of prior distributions on the design of sampling plans is also investigated through a computational study.

• Journal of Korean Society for Quality Management
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• v.34 no.1
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• pp.48-53
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• 2006

Applying Bayesian optimal design principles is not easy when a prior distribution is not certain. We present a optimal design criterion which possibly yield a reasonably good design and also robust with respect to misspecification of the prior distributions. The criterion is applied to the problem of estimating the turning point of a quadratic regression. Exact mathematical results are presented under certain conditions on prior distributions. Computational results are given for some cases not satisfying our conditions.

• Communications for Statistical Applications and Methods
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• v.15 no.1
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• pp.95-104
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• 2008

This paper addresses issues of robustness in Bayesian optimal design. We may have difficulty applying Bayesian optimal design principles because of the uncertainty of prior distribution. When there are several plausible prior distributions and the efficiency of a design depends on the unknown prior distribution, robustness with respect to misspecification of prior distribution is required. We suggest a new optimal design criterion which has relatively high efficiencies across the class of plausible prior distributions. The criterion is applied to the problem of estimating the turning point of a quadratic regression, and both analytic and numerical results are shown to demonstrate its robustness.

• Proceedings of the Korean Statistical Society Conference
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• 2002.05a
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• pp.67-72
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• 2002

A Bayesian testing procedure is proposed for assessment of bioequivalence in both mean and variance which ensures population bioequivalence under normality assumption. We derive the joint posterior distribution of the means and variances in a standard 2 ${\times}$ 2 crossover experimental design and propose a Bayesian testing procedure for bioequivalence based on a Markov chain Monte Carlo methods. The proposed method is applied to a real data set.

• Journal of the Korean Statistical Society
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• v.32 no.2
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• pp.175-191
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• 2003

An empirical Bayesian approach is discussed for estimation of characteristics from the two-way balanced rotation sampling design which includes U.S. Current Population Survey and Canadian Labor Force Survey as special cases. An empirical Bayesian estimator is derived for monthly effect under presence of two types of biases and correlations It is shown that the marginal distribution of observation provides more general correlation structure than that frequentist has assumed. Consistent estimators are derived for hyper-parameters in Normal priors.

• Journal of Korea Water Resources Association
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• v.47 no.5
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• pp.469-482
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• 2014

This study developed a Bayesian spatial regional frequency analysis, which aimed to analyze spatial patterns of design rainfall by incorporating geographical information (e.g. latitude, longitude and altitude) and climate characteristics (e.g. annual maximum series) within a Bayesian framework. There are disadvantages to considering geographical characteristics and to increasing uncertainties associated with areal rainfall estimation on the existing regional frequency analysis. In this sense, this study estimated the parameters of Gumbel distribution which is a function of geographical and climate characteristics, and the estimated parameters were spatially interpolated to derive design rainfall over the entire Han-river watershed. The proposed Bayesian spatial regional frequency analysis model showed similar results compared to L-moment based regional frequency analysis, and even better performance in terms of quantifying uncertainty of design rainfall and considering geographical information as a predictor.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.6A
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• pp.561-572
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• 2010

This paper is a study about application of recently proposed Performance Based Mixture Design (PBMD) for design of high strength concrete (HSC) to obtain HSC mix proportion that satisfies required performances. The PBMD method which uses Satisfaction curve based on a Bayesian method is a performance oriented concrete mix proportion design procedure easily applicable to any condition and environment for a possible replacement to the current prescriptive design standards. Based on extensive experimental results obtained for various materials and performance parameters of HSC, the application feasibility of the developed PBMD procedure for HSC has been verified. Also, the proposed PBMD procedure has been used to perform application examples to obtain desired target performances of HSC with optimum concrete mixture proportions using locally available materials, local environmental conditions, and available concrete production technologies. The validity and precision of HSC mix proportion design obtained using the PBMD method is verified with the experimental and ACI presented results to check the feasibility for actual design usage.