• Communications for Statistical Applications and Methods
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• 제17권4호
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• pp.551-560
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• 2010

This paper considers a Bayesian approach to modeling a flexible regression function under structural measurement error model. The regression function is modeled based on semiparametric regression with penalized splines. Model fitting and parameter estimation are carried out in a hierarchical Bayesian framework using Markov chain Monte Carlo methodology. Their performances are compared with those of the estimators under structural measurement error model without a semiparametric component.

• Journal of the Korean Data and Information Science Society
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• 제14권3호
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• pp.441-450
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• 2003

Collaborative filtering is one of the most widely used methodologies for recommendation system. Collaborative filtering is based on a data matrix of each customer's preferences and frequently, there exits missing data problem. We introduced two imputation approach (multiple imputation via Markov Chain Monte Carlo method and multiple imputation via bootstrap method) to improve the prediction performance of collaborative filtering and evaluated the performance using EachMovie data.

• 한국정보보호학회:학술대회논문집
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• 한국정보보호학회 2001년도 종합학술발표회논문집
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• pp.269-272
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• 2001

본 논문에서는 베이지안 네트워크와 통합 감사자료를 이용하여 시스템 사용자에 대한 비정상행위를 탐지하고 분석하는데 효과적인 모델을 제안하고자 한다. 이를 위해 리눅스 시스템에서의 여러 가지 감사자료들을 통합한 감사자료로부터 사용자의 행위에 대해 베이지안 네트워크로 구성하고자 한다. 베이지안 네트워크를 구성할 때 효율적인 학습이 가능한 Sparse Candidate 알고리즘을 적용하고, 감사자료의 일부가 결여되어 있는 경우에도 추론이 가능하도록 MCMC(Markov Chain Monte Carlo)의 일종인 Gibbs Sampling 방법을 적용한다.

• Communications for Statistical Applications and Methods
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• 제7권3호
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• pp.817-827
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• 2000

In this paper, we investigate the Bayesian approach to random effect binomial regression models with improper prior due to the absence of information on parameter. We also propose a method of estimating the posterior moments and prediction and discuss some general methods for studying model assessment. The methodology is illustrated with Crowder's Seeds Data. Markov Chain Monte Carlo techniques are used to overcome the computational difficulties.

• Journal of the Korean Statistical Society
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• 제31권1호
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• pp.45-61
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• 2002

We consider the problem of estimating binomial proportions in the presence of nonignorable nonresponse using the Bayesian selection approach. Inference is sampling based and Markov chain Monte Carlo (MCMC) methods are used to perform the computations. We apply our method to study doctor visits data from the Korean National Family Income and Expenditure Survey (NFIES). The ignorable and nonignorable models are compared to Stasny's method (1991) by measuring the variability from the Metropolis-Hastings (MH) sampler. The results show that both models work very well.

• Journal of the Korean Statistical Society
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• 제31권1호
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• pp.93-107
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• 2002

Random effects generalised linear models are useful for analysing clustered count data in which responses are usually correlated. We propose a Bayesian approach to parameter estimation and variable selection in random effects generalised linear models for count data. A simple Gibbs sampling algorithm for parameter estimation is presented and a simple and efficient variable selection is done by using the Gibbs outputs. An illustrative example is provided.

• Communications for Statistical Applications and Methods
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• 제20권6호
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• pp.427-438
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• 2013

Based on progressive Type II censored sampling which is an important method to obtain failure data in a lifetime study, we suggest a very general form of Bayesian prediction bounds from two parameters exponentiated Weibull distribution using the proper general prior density. For this, Markov chain Monte Carlo approach is considered and we also provide a simulation study.

• Journal of the Korean Data and Information Science Society
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• 제15권3호
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• pp.605-616
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• 2004

In this paper we consider the proportional hazard models for survival analysis in the microarray data. For a given vector of response values and gene expressions (covariates), we address the issue of how to reduce the dimension by selecting the significant genes. In our approach, rather than fixing the number of selected genes, we will assign a prior distribution to this number. To implement our methodology, we use a Markov Chain Monte Carlo (MCMC) method.

• Journal of the Korean Data and Information Science Society
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• 제21권2호
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• pp.379-385
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• 2010

This paper considers Bayesian approach to modeling a flexible regression function under functional measurement error model. The regression function is modeled based on semiparametric regression with penalized splines. Model fitting and parameter estimation are carried out in a hierarchical Bayesian framework using Markov chain Monte Carlo methodology. Their performances are compared with those of the estimators under functional measurement error model without semiparametric component.

• Journal of applied mathematics & informatics
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• 제42권4호
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• pp.819-829
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• 2024

This paper delves into an examination of both non-Bayesian and Bayesian estimation techniques for determining the Topp-leone inverse Weibull distribution parameters based on progressive Type-II censoring. The first approach employs expectation maximization (EM) algorithms to derive maximum likelihood estimates for these variables. Subsequently, Bayesian estimators are obtained by utilizing symmetric and asymmetric loss functions such as Squared error and Linex loss functions. The Markov chain Monte Carlo method is invoked to obtain these Bayesian estimates, solidifying their reliability in this framework.