• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권6호
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• pp.2996-3011
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• 2017

With rapid growth of web technology and dissemination of smart devices, social networking service(SNS) is widely used. As a result, huge amount of data are generated from SNS such as Twitter, and sentiment analysis of SNS data is very important for various applications and services. In the existing sentiment analysis based on the $Na{\ddot{i}}ve$ Bayes algorithm, a same number of attributes is usually employed to estimate the weight of each class. Moreover, uncountable and meaningless attributes are included. This results in decreased accuracy of sentiment analysis. In this paper two methods are proposed to resolve these issues, which reflect the difference of the number of positive words and negative words in calculating the weights, and eliminate insignificant words in the feature selection step using Multinomial $Na{\ddot{i}}ve$ Bayes(MNB) algorithm. Performance comparison demonstrates that the proposed scheme significantly increases the accuracy compared to the existing Multivariate Bernoulli $Na{\ddot{i}}ve$ Bayes(BNB) algorithm and MNB scheme.

• Journal of the Korean Data and Information Science Society
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• 제26권6호
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• pp.1573-1582
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• 2015

In lifetime data analysis, it is generally known that the lifetimes of test items may not be recorded exactly. There are also situations wherein the withdrawal of items prior to failure is prearranged in order to decrease the time or cost associated with experience. Moreover, it is generally known that more than one cause or risk factor may be present at the same time. Therefore, analysis of censored competing risks data are needed. In this article, we derive the Bayes estimators for the entropy function under the exponential distribution with an unknown scale parameter based on multiply Type II censored competing risks data. The Bayes estimators of entropy function for the exponential distribution with multiply Type II censored competing risks data under the squared error loss function (SELF), precautionary loss function (PLF) and DeGroot loss function (DLF) are provided. Lindley's approximate method is used to compute these estimators.We compare the proposed Bayes estimators in the sense of the mean squared error (MSE) for various multiply Type II censored competing risks data. Finally, a real data set has been analyzed for illustrative purposes.

• Journal of applied mathematics & informatics
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• 제3권1호
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• pp.55-64
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• 1996

Let $X_1, ....$X_P be p($\geq$2) independent random variables, where each X1 has a gamma distribution with $k_i and ${\heta}_i$. The problem is to simultaneously estimate p gammar parameters ${\heta}_i$ under entropy loss where the parameters are believed priori. Hierarchical bayes(HB) and empirical bayes(EB) estimators are investigated. Next computer simulation is studied to compute the risk percentage improvement of the HB, EB and the estimator of Dey et al.(1987) compared to MVUE of ${\heta}$.

• 품질경영학회지
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• 제26권4호
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• pp.265-276
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• 1998

A commercial nuclear power station contains at least two emergency diesel generators(EDG) to control the risk of severe core damage during station blackout accidnets. Therefore, thereliability of the EDG's to start and load-run on demand must be maintained at a sufficiently high level. Until now, a simple assessment of start and load-run success rates was used to calculate the EDG reliability. However, this method has been found to contain many defects. Recently, the work of Martz et al.(1996) proposed the use of the Bayes estimator to find EDG reliability. Shim(1996) proposed a confidence interval for the Bayes estimator, compare the above two methods. In this paper, we introduce the notion of "Composite Reliablility" to estimate the reliability of nuclear-power plant EDG, and using practical examples, illustrate which method is more a, pp.opriate in our situation.situation.

• Journal of the Korean Statistical Society
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• 제25권4호
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• pp.529-544
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• 1996

This paper deals with the problem of estimating the autoregressive random coefficient of a first-order random coefficient autoregressive time series model applied to panel data of time series. The autoregressive random coefficients across individual units are assumed to be a random sample from a truncated normal distribution with the space (-1, 1) for stationarity. The estimates of random coefficients are obtained by an empirical Bayes procedure using the estimates of model parameters. Also, a Monte Carlo study is conducted to support the estimation procedure proposed in this paper. Finally, we apply our results to the economic panel data in Liu and Tiao(1980).

• 한국수자원학회논문집
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• 제50권7호
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• pp.489-502
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• 2017

본 연구에서는 충주댐 유역에 대해 다목적 댐 예측유입량 산정기법 BAYES-ESP를 개발하고 평가하였다. BAYES-ESP 기법은 기존 ESP (Ensemble Streamflow Prediction) 기법에 베이지안 이론을 적용하여 개발하였으며, 수문모델은 ABCD를 활용하였다. 입력자료는 기온, 강수량 자료와 댐 관측유입량 자료를 활용하였으며, 기온 및 강수량은 기상청, 국토교통부, 한국수자원공사의 지점관측자료, 댐 관측유입량은 한국수자원공사의 자료를 이용하였다. 적용성 평가방법은 시계열 분석과 Skill Score를 활용하였으며, 평가기간은 1986~2015년이다. 시계열 분석 결과 ESP 댐 예측유입량(ESP)는 매년 전망값의 큰 차이가 없었으며, 다우년 및 과우년의 예측성이 떨어지는 것으로 나타났다. BAYES-ESP 댐 예측유입량(BAYES-ESP)는 ESP가 관측유입량에 비해 과소모의하는 경향을 보정하였으며, 특히 다우년에 개선효과가 있는 것으로 나타났다. 월별 평균 댐 관측유입량과의 Skill Score 비교분석결과 ESP는 1~3월에 SS가 비교적 높은 값을 보였으며, 나머지 월에는 음의 값을 나타내었다. BAYES-ESP는 ESP와 관측 값 간의 선형적 관계를 갖는 1~3월에 ESP의 정확도를 향상시키는 것으로 나타났다. ESP 기법은 국내 강수특성상 우리나라에 적용하기에는 한계가 있었으며, 이를 개선한 BAYES-ESP 기법은 댐 유입량 예측연구에 가치가 있다고 판단된다.

• 품질경영학회지
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• 제18권2호
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• pp.101-116
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• 1990

The objectives of this thesis are : first, to estimate the parameters and Pr[X < Y] in the Marshall and Olkin's Bivariate Exponential Distribution ; and secondly, to compare the Bayes estimators of Pr[X < Y] with maximum likelihood estimator of Pr[X < Y] in the Marshall and Olkin's Bivariate Exponential Distribution. Through the Monte Carlo Simulation, we observed that the Bayes estimators of Pr[X < Y] perform better than the maximum likelihood estimator of Pr[X < Y] and the Bayes estimator of Pr[X < Y] with gamma prior distribution performs better than with vague prior distribution with respect to bias and mean squared error in the Marshall and Olkin's Bivariate Exponential Distribution.

• Communications for Statistical Applications and Methods
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• 제8권2호
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• pp.453-464
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• 2001

A commercial nuclear power station contains at least tow emergency diesel generators(EDG) to control the risk of severe core damage during station blackout accidents. Therefore, the reliability of the EDG's to start and load-run on demand must be maintained at a sufficiently high level. Probabilistic safety assessments(PSA) are increasingly being used to quantify the public risk of operating potentially hazardous systems such as nuclear power reactors. In this paper, to perform PSA, we will introduce three different types of data and use Bayes procedure to estimate the error rate of nuclear power plant EDG, and using practical examples, illustrate which method is more reasonable in our situation.

• Communications for Statistical Applications and Methods
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• 제6권1호
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• pp.221-235
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• 1999

Let be $X_1$,...,$X_p$, $p\geq2$ independent random variables where each $X_i$ has a gamma distribution with $\textit{k}_i$ and $\theta_i$ The problem is to simultaneously estimate $\textit{p}$ gamma parameters $\theta_i$ and $\theta_i{^-1}$ under entropy loss where the parameters are believed priori. Hierarch ical Bayes(HB) and empirical Bayes(EB) estimators are investigated. And a preference of HB estimator over EB estimator is shown using Gibbs sampler(Gelfand and Smith 1990). Finally computer simulation is studied to compute the risk percentage improvements of the HB estimator and the estimator of Dey Ghosh and Srinivasan(1987) compared to UMVUE estimator of $\theta^{-1}$.

• Communications for Statistical Applications and Methods
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• 제10권3호
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• pp.981-996
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• 2003

Bayesian inference is considered for switching mean models with the ARMA errors. We use noninformative improper priors or uniform priors. The fractional Bayes factor of O'Hagan (1995) is used as the Bayesian tool for detecting the existence of a single change or multiple changes and the usual Bayes factor is used for identifying the orders of the ARMA error. Once the model is fully identified, the Gibbs sampler with the Metropolis-Hastings subchains is constructed to estimate parameters. Finally, we perform a simulation study to support theoretical results.