• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권2호
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• pp.438-455
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• 2024

Recently, many unsupervised learning-based models have emerged for Just Noticeable Difference (JND) estimation, demonstrating remarkable improvements in accuracy. However, these models suffer from a significant drawback is that their heavy reliance on handcrafted priors for guidance. This restricts the information for estimating JND simply extracted from regions that are highly related to handcrafted priors, while information from the rest of the regions is disregarded, thus limiting the accuracy of JND estimation. To address such issue, on the one hand, we extract the information for estimating JND in an Adversarial Complementary Learning (ACoL) way and propose an ACoL-JND network to estimate the JND by comprehensively considering the handcrafted priors-related regions and non-related regions. On the other hand, to make the handcrafted priors richer, we take two additional priors that are highly related to JND modeling into account, i.e., Patterned Masking (PM) and Contrast Masking (CM). Experimental results demonstrate that our proposed model outperforms the existing JND models and achieves state-of-the-art performance in both subjective viewing tests and objective metrics assessments.

• Communications for Statistical Applications and Methods
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• 제2권2호
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• pp.63-73
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• 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 Statistical Society
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• 제27권3호
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• pp.331-348
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• 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.

• 응용통계연구
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• 제26권5호
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• pp.737-746
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• 2013

Tobin (1958)에 의해 처음 소개된 절단 회귀모형에서 베이지안 추정은 최대가능도 추정보다 실제값에 가까운 것으로 알려져 있으나 베이지안 방법론이 구간추정 문제에 있어서도 성공적으로 작동할 수 있을 지에 대해서는 알려진 바가 없다. 일반적으로 베이지안 방법론에서 사전분포는 분석자의 사전정보를 반영하기 때문에 주관적인 분석이 될 수 밖에 없는데, 이렇게 주관적인 분석에서는 빈도학파들이 요구하는 기준을 따르기 어렵다. 그러나 무정보사전분포는 때때로 빈도학파적 특성을 갖는 베이지안 추론을 가능하게 한다. 본 연구에서는 절단 회귀모형에서 무정보사전분포에 의한 베이지안 신뢰구간의 빈도학파적 특성을 살펴보고 최대가능도 추정 신뢰구간과 포함확률을 비교한다. 이를 통해 최대가능도 추정의 표준오차가 과소 추정되고 있음 밝힌다.

• 대한지리학회지
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• 제49권5호
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• pp.761-778
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• 2014

본 논문은 거래빈도가 낮아 지금껏 적극적으로 시도되지 못한 상업용 토지의 가격을 정확히 추정하고자 하였다. 서울시 상업용 토지 실거래가 자료를 대상으로 선형 결합 형태의 평균 구조(전역적 경향), 지수 형태의 공분산함수 그리고 순수 오차항을 구성요소로 하는 모형을 구축 및 적용하였다. 상권별로 가격수준이 차별적으로 형성되는 상업용 토지 가격의 특성을 감안하여 대표적 공간보간기법인 크리깅 방법을 적용함으로써 지가의 공간적 상관성을 명시적으로 고려하였다. 더 나아가 희소한 자료의 한계를 극복하기 위해 전문가 지식을 사전 확률분포의 형태로 모형에 반영할 수 있는 베이지안 크리깅 방법을 활용하였다. 적용한 모형의 성능은 적합 과정에 사용되지 않은 검증 자료를 대상으로 검토하였으며, 전문가 지식의 반영과 공간적 상관성의 명시적 고려를 통해 가격 추정의 정확성이 높아진 사실을 확인하였다. 본 논문은 베이지안 크리깅 기법을 토지 가격 추정에 적용하되, 전문가의 주관적 지식을 명시적으로 모형에 반영하였다는 점 등에서 기존 연구와 차별성을 갖는다. 본 논문의 결과는 거래 자료가 희소한 상황에서도 신뢰성 있게 부동산 가격을 추정해야하는 경우에 유용하게 활용될 수 있을 것으로 기대된다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권8호
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• pp.2851-2865
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• 2014

To improve the rate-distortion performance of distributed video compressive sensing (DVCS), the adaptive sparse basis and nonlocal similarity of video are proposed to jointly reconstruct the video signal in this paper. Due to the lack of motion information between frames and the appearance of some noises in the reference frames, the sparse dictionary, which is constructed using the examples directly extracted from the reference frames, has already not better obtained the sparse representation of the interpolated block. This paper proposes a method to construct the sparse dictionary. Firstly, the example-based data matrix is constructed by using the motion information between frames, and then the principle components analysis (PCA) is used to compute some significant principle components of data matrix. Finally, the sparse dictionary is constructed by these significant principle components. The merit of the proposed sparse dictionary is that it can not only adaptively change in terms of the spatial-temporal characteristics, but also has ability to suppress noises. Besides, considering that the sparse priors cannot preserve the edges and textures of video frames well, the nonlocal similarity regularization term has also been introduced into reconstruction model. Experimental results show that the proposed algorithm can improve the objective and subjective quality of video frame, and achieve the better rate-distortion performance of DVCS system at the cost of a certain computational complexity.

• 한국수산과학회지
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• 제55권2호
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• pp.183-188
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• 2022

It is a difficult task to estimate parameters in even a simple stock assessment model such as a surplus production model, using only data about temporal catch-per-unit-effort (CPUE) (or survey index) and fishery yields. Such difficulty is exacerbated when time-varying parameters are treated as random effects (aka state variables). To overcome the difficulty, previous studies incorporated somewhat subjective assumptions (e.g., B₁=K) or informative priors of parameters. A key is how to build an objective joint prior of parameters, reducing subjectivity. Given the limited data on temporal CPUEs and fishery yields from 1999-2020 for common squid Todarodes pacificus, we built a joint prior of only two parameters, intrinsic growth rate (r) and carrying capacity (K), based on the resilience level of the population (Froese et al., 2017), and used a Bayesian state-space production assessment model. We used template model builder (TMB), a R package for implementing the assessment model, and estimating all parameters in the model. The predicted annual biomass was in the range of 0.76×10⁶ to 4.06×10⁶ MT, the estimated MSY was 0.13×10⁶ MT, the estimated r was 0.24, and the estimated K was 2.10×10⁶ MT.