• Journal of the Korean Statistical Society
• /
• 제27권3호
• /
• pp.331-348
• /
• 1998

In this paper, we have proposed some robust Bayes estimators using ML-II priors as well as certain empirical Bayes estimators in estimating the finite population mean in the presence of auxiliary information. These estimators are compared with the classical ratio estimator and a subjective Bayes estimator utilizing the auxiliary information in terms of "posterior robustness" and "procedure robustness" Also, we have addressed the issue of choice of sampling design from a robust Bayesian viewpoint.

• Communications for Statistical Applications and Methods
• /
• 제2권2호
• /
• pp.63-73
• /
• 1995

We consider some robust Bayes estimators using ML-II priors as well as certain empirical Bayes estimators in estimating the finite population mean. The proposed estimators are compared with the sample mean and subjective Bayes estimators in terms of "posterior robustness" and "procedure robustness".re robustness".uot;.

• Journal of the Korean Data and Information Science Society
• /
• 제17권4호
• /
• pp.1309-1317
• /
• 2006

The paper considers some Bayes estimators of the finite population mean with auxiliary information under priors which are scale mixtures of normal, and thus have tail heavier than that of the normal. The proposed estimators are quite robust in general. Numerical methods of finding Bayes estimators under these heavy tailed priors are given, and are illustrated with an actual example.

• Communications for Statistical Applications and Methods
• /
• 제6권2호
• /
• pp.501-510
• /
• 1999

In this paper, we consider a comparable study on three Bayes procedures for the multivariate normal mean estimation problem. In specific we consider hierarchical Bayes empirical Bayes and robust Bayes estimators for the normal means. Then three procedures are compared in terms of the four comparison criteria(i.e. Average Relative Bias (ARB) Average Squared Relative Bias (ASRB) Average Absolute Bias(AAB) Average Squared Deviation (ASD) using the real data set.

• The Journal of Asian Finance, Economics and Business
• /
• 제5권3호
• /
• pp.19-29
• /
• 2018

This research examined the alternatives of Jensen's alpha (α) estimation models in the Capital Asset Pricing Model, discussed by Treynor (1961), Sharpe (1964), and Lintner (1965), using the robust maximum likelihood type m-estimator (MM estimator) and Bayes estimator with conjugate prior. According to finance literature and practices, alpha has often been estimated using ordinary least square (OLS) regression method and monthly return data set. A sample of 50 securities is randomly selected from the list of the S&P 500 index. Their daily and monthly returns were collected over a period of the last five years. This research showed that the robust MM estimator performed well better than the OLS and Bayes estimators in terms of efficiency. The Bayes estimator did not perform better than the OLS estimator as expected. Interestingly, we also found that daily return data set would give more accurate alpha estimation than monthly return data set in all three MM, OLS, and Bayes estimators. We also proposed an alternative market efficiency test with the hypothesis testing Ho: α = 0 and was able to prove the S&P 500 index is efficient, but not perfect. More important, those findings above are checked with and validated by Jackknife resampling results.

• Communications for Statistical Applications and Methods
• /
• 제21권3호
• /
• pp.261-274
• /
• 2014

In this paper we develop Bayes and empirical Bayes estimators of the finite population mean with the assumption of posterior linearity rather than normality of the superpopulation under the balanced loss function. We compare the performance of the optimal Bayes estimator with ones of the classical sample mean and the usual Bayes estimator under the squared error loss with respect to the posterior expected losses, risks and Bayes risks when the underlying distribution is normal as well as when they are binomial and Poisson.

• Communications for Statistical Applications and Methods
• /
• 제28권4호
• /
• pp.315-327
• /
• 2021

The Spatial Autoregressive (SAR) models have drawn considerable attention in recent econometrics literature because of their capability to model the spatial spill overs in a feasible way. While considering the Bayesian analysis of these models, one may face the problem of lack of robustness with respect to underlying prior assumptions. The generalized Bayes estimators provide a viable alternative to incorporate prior belief and are more robust with respect to underlying prior assumptions. The present paper considers the SAR model with a set of linear restrictions binding the regression coefficients and derives restricted generalized Bayes estimator for the coefficients vector. The minimaxity of the restricted generalized Bayes estimator has been established. Using a simulation study, it has been demonstrated that the estimator dominates the restricted least squares as well as restricted Stein rule estimators.

• 대한전자공학회논문지TC
• /
• 제46권5호
• /
• pp.70-77
• /
• 2009

하나의 reader와 여러 tag로 구성된 RFID 망에서 tag의 응답 간 충돌을 중재하기 위해 tag가 응답하도록 여러 슬롯을 마련해 주는 프레임화 및 슬롯화된 ALOHA 방식이 소개되었다. 프레임화 및 슬롯화된 ALOHA에서는 tag 인식의 효율이 극대화되기 위해 프레임 별 슬롯의 수가 최적화되어야 한다. 이러한 최적화는 tag의 수를 필요로 하나 reader는 tag의 수를 알기 힘들다. 본 논문에서는 별도로 tag의 수를 추정하지 않고 슬롯의 수에 대해 직접 Bayes action을 취하는 프레임화 및 슬롯화된 ALOHA에 기초한 tag 인식 방식을 제안한다. 구체적으로 Bayes action은 tag의 수가 갖는 사전 분포, 어떤 tag도 응답하지 않은 슬롯의 수에 대한 관찰값, 그리고 인식률을 반영한 손실 함수를 결합한 결정 문제를 풀어 구한다. 또한 tag의 수가 갖는 사전 분포의 진화를 통해 각 프레임에서 이러한 Bayes action을 지원한다. 모의 실험 결과로부터 진화하는 사전 분포와 Bayes action의 쌍은 robust 방식을 이루어 tag의 수의 참값과 초기 추측값의 큰 괴리에도 불구하고 일정 수준의 인식률을 얻을 수 있음을 관찰한다. 또한 제안하는 방식은 tag의 수에 대한 고전적인 추정값을 사용하는 방식에 비해 높은 인식 완료 확률을 얻을 수 있음을 확인한다.

• Journal of the Korean Data and Information Science Society
• /
• 제25권3호
• /
• pp.685-696
• /
• 2014

In this paper, we develop Bayesian inference of the finite population mean with the assumption of posterior linearity rather than normality of the superpopulation in the presence of auxiliary information under the balanced loss function. We compare the performance of the optimal Bayes estimator under the balanced loss function with ones of the classical ratio estimator and the usual Bayes estimator in terms of the posterior expected losses, risks and Bayes risks.

• Interdisciplinary Bio Central
• /
• 제2권2호
• /
• pp.4.1-4.6
• /
• 2010

The most common type of microarray experiment has a simple design using microarray data obtained from two different groups or conditions. A typical method to identify differentially expressed genes (DEGs) between two conditions is the conventional Student's t-test. The t-test is based on the simple estimation of the population variance for a gene using the sample variance of its expression levels. Although empirical Bayes approach improves on the t-statistic by not giving a high rank to genes only because they have a small sample variance, the basic assumption for this is same as the ordinary t-test which is the equality of variances across experimental groups. The t-test and empirical Bayes approach suffer from low statistical power because of the assumption of normal and unimodal distributions for the microarray data analysis. We propose a method to address these problems that is robust to outliers or skewed data, while maintaining the advantages of the classical t-test or modified t-statistics. The resulting data transformation to fit the normality assumption increases the statistical power for identifying DEGs using these statistics.