• 응용통계연구
• /
• 제24권6호
• /
• pp.1149-1160
• /
• 2011

기존의 frequentist 추론에 비해 Bayesian 추론에서의 가설 검정 및 모형 선택 문제는 학자들 간에 일치된 견해를 보이지 못하고 있으며 아직도 논란이 되는 것들이 많다. Bayesian 추론에서 가설 검정 및 모형 선택의 기준으로 널리 쓰이는 Bayes factor는 이해하기 쉬우나 여러 경우에 구하기 어려운 단점이 존재한다. 그 외에 다른 기준으로 Spiegelhalter 등 (2002)가 제시한 DIC(Deviance Information Criterion)과 frequentist 추론에서의 P-value에 대비되는 Bayesian P-value가 있다. 본 논문에서는 Swiss banknote 자료를 Bayesian 로지스틱 회귀모형으로 분석하고 관련 기준들을 구하여 각 기준들이 일관성 있는 결론을 보이는지 확인하고자 한다.

• Journal of the Korean Data and Information Science Society
• /
• 제20권6호
• /
• pp.1191-1197
• /
• 2009

Most Bayesian approaches to model selection of wavelet analysis have drawbacks that computational cost is expensive to obtain accuracy for the fitted unknown function. To overcome the drawback, this article introduces loss functions which are criteria for level dependent threshold selection in wavelet based Bayesian methods with arbitrary size and regular design points. We demonstrate the utility of these criteria by four test functions and real data.

• Journal of the Korean Data and Information Science Society
• /
• 제24권6호
• /
• pp.1543-1550
• /
• 2013

In this paper, we study Bayesian estimation for the finite population proportion in binary data under selection bias. We use a Bayesian nonignorable selection model to accommodate the selection mechanism. We compare four possible estimators of the finite population proportions based on data analysis as well as Monte Carlo simulation. It turns out that nonignorable selection model might be useful for weekly biased samples.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제6권4호
• /
• pp.277-281
• /
• 2006

Cyber counseling, one of the most compatible type of consultation for the information society, enables people to reveal their mental agonies and private problems anonymously, since it does not require face-to-face interview between a counsellor and a client. However, there are few cyber counseling centers which provide high quality and trustworthy service, although the number of cyber counseling center has highly increased. Therefore, this paper is intended to enable an appropriate consultation for each client by analyzing client propensity using Bayesian variable selection. Bayesian variable selection is superior to stepwise regression analysis method in finding out a regression model. Stepwise regression analysis method, which has been generally used to analyze individual propensity in linear regression model, is not efficient since it is hard to select a proper model for its own defects. In this paper, based on the case database of current cyber counseling centers in the web, we will analyze clients' propensities using Bayesian variable selection to enable individually target counseling and to activate cyber counseling programs.

• 천문학회보
• /
• 제46권1호
• /
• pp.48.2-48.2
• /
• 2021

We have developed a frequentist approach for model selection which determines consistency of a cosmological model and the data using the distribution of likelihoods from the iterative smoothing method. Using this approach, we have shown how confidently we can distinguish different models without comparison with one another. In this current work, we compare our approach with conventional Bayesian approach based on estimation of Bayesian evidence using nested sampling for the purpose of model selection. We use simulated future Roman (formerly WFIRST)-like type Ia supernovae data in our analysis. We discuss limits of the Bayesian approach for model selection and display how our proposed frequentist approach, if implemented appropriately, can perform better in falsification of individual models.

• Journal of the Korean Data and Information Science Society
• /
• 제18권4호
• /
• pp.1123-1134
• /
• 2007

This article addresses the problem of testing whether the shape parameters in k independent Weibull populations are equal. We propose a Bayesian model selection procedure for equality of the shape parameters. The noninformative prior is usually improper which yields a calibration problem that makes the Bayes factor to be defined up to a multiplicative constant. So we propose the objective Bayesian model selection procedure based on the fractional Bayes factor and the intrinsic Bayes factor under the reference prior. Simulation study and a real example are provided.

• Communications for Statistical Applications and Methods
• /
• 제29권3호
• /
• pp.287-299
• /
• 2022

In radiation epidemiology, the excess relative risk (ERR) model is used to determine the dose-response relationship. In general, the dose-response relationship for the ERR model is assumed to be linear, linear-quadratic, linear-threshold, quadratic, and so on. However, since none of these functions dominate other functions for expressing the dose-response relationship, a Bayesian semiparametric method using splines has recently been proposed. Thus, we improve the Bayesian semiparametric method for the selection of the tuning parameters for splines as the number and location of knots using a Bayesian knot selection method. Equally spaced knots cannot capture the characteristic of radiation exposed dose distribution which is highly skewed in general. Therefore, we propose a nonparametric Bayesian knot selection method based on a Dirichlet process mixture model. Inference of the spline coefficients after obtaining the number and location of knots is performed in the Bayesian framework. We apply this approach to the life span study cohort data from the radiation effects research foundation in Japan, and the results illustrate that the proposed method provides competitive curve estimates for the dose-response curve and relatively stable credible intervals for the curve.

• 한국정보처리학회논문지
• /
• 제7권3호
• /
• pp.852-861
• /
• 2000

Software reliability growth models are used in testing stages of software development to model the error content and time intervals betwewn software failures. In this paper, using priors for the number of fault with the negative binomial distribution nd the error rate with gamma distribution, Bayesian inference and model selection method for Jelinski-Moranda and Goel-Okumoto and Schick-Wolverton models in software reliability. For model selection, we explored the sum of the relative error, Braun statistic and median variation. In Bayesian computation process, we could avoid the multiple integration by the use of Gibbs sampling, which is a kind of Markov Chain Monte Carolo method to compute the posterior distribution. Using simulated data, Bayesian inference and model selection is studied.

• Journal of the Korean Data and Information Science Society
• /
• 제25권1호
• /
• pp.187-194
• /
• 2014

We consider a Bayesian nonignorable selection model to accommodate the selection bias. Markov chain Monte Carlo methods is known to be very useful to fit the nonignorable selection model. However, sensitivity to prior assumptions on parameters for selection mechanism is a potential problem. To quantify the sensitivity to prior assumption, the deviance information criterion and the conditional predictive ordinate are used to compare the goodness-of-fit under two different prior specifications. It turns out that the 'MLE' prior gives better fit than the 'uniform' prior in viewpoints of goodness-of-fit measures.

• Communications for Statistical Applications and Methods
• /
• 제4권1호
• /
• pp.259-269
• /
• 1997

Bayesian inference for a record value statistics(RVS) model of nonhomogeneous Poisson process is considered. We seal with Bayesian inference for double exponential, Gamma, Rayleigh, Gumble RVS models using Gibbs sampling and Metropolis algorithm and also explore Bayesian computation and model selection.