• 제목/요약/키워드: bias, variance components.

검색결과 16건 처리시간 0.023초

Influence of Inbreeding Depression on Genetic (Co)Variance and Sire-by-Year Interaction Variance Estimates for Weaning Weight Direct-Maternal Genetic Evaluation

  • Lee, C.;Pollak, E.J.
    • Asian-Australasian Journal of Animal Sciences
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    • 제10권5호
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    • pp.510-513
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    • 1997
  • This study examined the effects of ignoring inbreeding depression on (co)variance components for weaning weight through the use of Monte Carlo simulation. Weaning weight is of particular interest as a trait for which additive direct and maternal genetic components exist and there then is the potential for a direct-maternal genetic covariance. Ignoring inbreeding depression in the analytical model (.8 kg reduction of phenotypic value per 1% inbreeding) led to biased estimates of all genetic (co) variance components, all estimates being larger than the true values of the parameters. In particular, a negative bias in the direct-maternal genetic covariance was observed in analyses that ignored inbreeding depression. A small spurious sire-by-year interaction variance was also observed.

쌍대반응표면 최적화에서 편차와 분산의 가중치 결정에 관한 연구 (Determining the Relative Weights of Bias and Variance in Dual Response Surface Optimization)

  • 정인준;김광재;장수영
    • 한국경영과학회:학술대회논문집
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    • 대한산업공학회/한국경영과학회 2004년도 춘계공동학술대회 논문집
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    • pp.294-297
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    • 2004
  • Mean squared error (MSE) is an effective criterion to combine the mean and the standard deviation responses in dual response surface optimization. The bias and variance components of MSE need to be weighted properly in the given problem situation. This paper proposes a systematic method to determine the relative weights of bias and variance in accordance with a decision maker's prior and posterior preference structure.

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ONNEGATIVE MINIMUM BIASED ESTIMATION IN VARIANCE COMPONENT MODELS

  • Lee, Jong-Hoo
    • East Asian mathematical journal
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    • 제5권1호
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    • pp.95-110
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    • 1989
  • In a general variance component model, nonnegative quadratic estimators of the components of variance are considered which are invariant with respect to mean value translaion and have minimum bias (analogously to estimation theory of mean value parameters). Here the minimum is taken over an appropriate cone of positive semidefinite matrices, after having made a reduction by invariance. Among these estimators, which always exist the one of minimum norm is characterized. This characterization is achieved by systems of necessary and sufficient condition, and by a cone restricted pseudoinverse. In models where the decomposing covariance matrices span a commutative quadratic subspace, a representation of the considered estimator is derived that requires merely to solve an ordinary convex quadratic optimization problem. As an example, we present the two way nested classification random model. An unbiased estimator is derived for the mean squared error of any unbiased or biased estimator that is expressible as a linear combination of independent sums of squares. Further, it is shown that, for the classical balanced variance component models, this estimator is the best invariant unbiased estimator, for the variance of the ANOVA estimator and for the mean squared error of the nonnegative minimum biased estimator. As an example, the balanced two way nested classification model with ramdom effects if considered.

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A Note on Performance of Conditional Akaike Information Criteria in Linear Mixed Models

  • Lee, Yonghee
    • Communications for Statistical Applications and Methods
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    • 제22권5호
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    • pp.507-518
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    • 2015
  • It is not easy to select a linear mixed model since the main interest for model building could be different and the number of parameters in the model could not be clearly defined. In this paper, performance of conditional Akaike Information Criteria and its bias-corrected version are compared with marginal Bayesian and Akaike Information Criteria through a simulation study. The results from the simulation study indicate that bias-corrected conditional Akaike Information Criteria shows promising performance when candidate models exclude large models containing the true model, but bias-corrected one prefers over-parametrized models more intensively when a set of candidate models increases. Marginal Bayesian and Akaike Information Criteria also have some difficulty to select the true model when the design for random effects is nested.

Investigation of Biases for Variance Components on Multiple Traits with Varying Number of Categories in Threshold Models Using Bayesian Inferences

  • Lee, D.H.
    • Asian-Australasian Journal of Animal Sciences
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    • 제15권7호
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    • pp.925-931
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    • 2002
  • Gibbs sampling algorithms were implemented to the multi-trait threshold animal models with any combinations of multiple binary, ordered categorical, and linear traits and investigate the amount of bias on these models with two kinds of parameterization and algorithms for generating underlying liabilities. Statistical models which included additive genetic and residual effects as random and contemporary group effects as fixed were considered on the models using simulated data. The fully conditional posterior means of heritabilities and genetic (residual) correlations were calculated from 1,000 samples retained every 10th samples after 15,000 samples discarded as "burn-in" period. Under the models considered, several combinations of three traits with binary, multiple ordered categories, and continuous were analyzed. Five replicates were carried out. Estimates for heritabilities and genetic (residual) correlations as the posterior means were unbiased when underlying liabilities for a categorical trait were generated given by underlying liabilities of the other traits and threshold estimates were rescaled. Otherwise, when parameterizing threshold of zero and residual variance of one for binary traits, heritability estimates were inflated 7-10% upward. Genetic correlation estimates were biased upward if positively correlated and downward if negatively correlated when underling liabilities were generated without accounting for correlated traits on prior information. Residual correlation estimates were, consequently, much biased downward if positively correlated and upward if negatively correlated in that case. The more categorical trait had categories, the better mixing rate was shown.

다중비교 절차를 이용한 제조공정의 분석 (Analysis of the Manufacturing Process using Multiple Comparison Procedure)

  • 최봉욱;김광섭
    • 산업경영시스템학회지
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    • 제20권44호
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    • pp.333-341
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    • 1997
  • The purpose of this paper is to compare the manufacturing process with random covariate using multiple comparison procedure. The methodology that compares each manufacturing process by inspecting the number of nonconforming items out of k-treatment, has serveral limitations and problems according to the method and contect of the analysis. The proper way of analysis, therefore, could be obtained by the multiple comparison procedure of simultaneous confidence region of variance components. Effections that affect a manufactuing process may be predictive of responce to treatments are called covariates. In the study of comparing several treatments, prsense of covariate may bias the estimates of treatment effects.

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AN EMPIRICAL BAYESIAN ESTIMATION OF MONTHLY LEVEL AND CHANGE IN TWO-WAY BALANCED ROTATION SAMPLING

  • Lee, Seung-Chun;Park, Yoo-Sung
    • Journal of the Korean Statistical Society
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    • 제32권2호
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    • pp.175-191
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    • 2003
  • An empirical Bayesian approach is discussed for estimation of characteristics from the two-way balanced rotation sampling design which includes U.S. Current Population Survey and Canadian Labor Force Survey as special cases. An empirical Bayesian estimator is derived for monthly effect under presence of two types of biases and correlations It is shown that the marginal distribution of observation provides more general correlation structure than that frequentist has assumed. Consistent estimators are derived for hyper-parameters in Normal priors.

해양모니터링 자료의 장기결측 보충 기법 (Long-gap Filling Method for the Coastal Monitoring Data)

  • 조홍연;이기섭;이욱재
    • 한국해안·해양공학회논문집
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    • 제33권6호
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    • pp.333-344
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    • 2021
  • 해양모니터링 자료에서 빈번하게 발생하는 장기결측구간의 자료 보충기법을 제안한다. 제안하는 방법은 결측구간의 장기변동 추세 성분과 단기변동 잔차성분을 추정하여 조합하는 방식으로 결측구간의 미지 정보를 추정한다. 이 방법을 이용하여 울릉도 해상부이 자료의 수온 항목, 약 1개월 정도의 장기결측 구간의 자료를 보충하였으며, 부이에서 관측하는 자료 항목에 대해서도 결측 보충을 수행하였다. 보충된 자료는 항목에 따라 차이를 보이지만 변동양상이 적절하게 재현되는 것으로 파악되었다. 이 방법은 추세추정과 잔차 반영에 따른 편향오차와 분산오차가 발생하지만, 장기결측으로 인한 통계적인 측도 추정의 편향오차는 크게 절감하는 것으로 파악되었다. 결측보충 모형의 추정 RMS 오차의 평균과 90% 신뢰구간은 각각 0.93, 0.35~1.95 범위이다.

Improved Attenuation Estimation of Ultrasonic Signals Using Frequency Compounding Method

  • Kim, Hyungsuk;Shim, Jaeyoon;Heo, Seo Weon
    • Journal of Electrical Engineering and Technology
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    • 제13권1호
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    • pp.430-437
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    • 2018
  • Ultrasonic attenuation is an important parameter in Quantitative Ultrasound and many algorithms have been proposed to improve estimation accuracy and repeatability for multiple independent estimates. In this work, we propose an improved algorithm for estimating ultrasonic attenuation utilizing the optimal frequency compounding technique based on stochastic noise model. We formulate mathematical compounding equations in the AWGN channel model and solve optimization problems to maximize the signal-to-noise ratio for multiple frequency components. Individual estimates are calculated by the reference phantom method which provides very stable results in uniformly attenuating regions. We also propose the guideline to select frequency ranges of reflected RF signals. Simulation results using numerical phantoms show that the proposed optimal frequency compounding method provides improved accuracy while minimizing estimation bias. The estimation variance is reduced by only 16% for the un-compounding case, whereas it is reduced by 68% for the uniformly compounding case. The frequency range corresponding to the half-power for reflected signals also provides robust and efficient estimation performance.

한국인 생활욕구의 요인구조 및 관련요인의 영향에 관한 연구 (A Study on the Factor Structure of Koreans' Need of Living and on the Influence of Related Variables)

  • 노영남
    • 대한가정학회지
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    • 제28권1호
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    • pp.67-87
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    • 1990
  • This study was designed with the purpose to analyze the bias and the factor structure of Need of Living and to clear the influence of related variables on Need on Living. The Degree of Importance(Di) and of sufficiency(Ds) of 65 indicators which were the constituent components of Need of Living and were extracted from the 51 preceeding studies was estimated by the 1084 samples including 614 urban and 470 rural residents through the questionaire. The indicators with higher Di and lower Ds than average were considered to show the high level of Need of Living. The main results are as follows; 1. Generally speaking, the level of Di was higher than that of Ds. Specially this was serious in the case of the indicators related with social-economic equity, employment, housing and environment. 2. Di level of the indicators such as physical health, judicial equality, the life of planned expenditure was highest. Specially the equity of income allocation was considered to be more important than the increase of income or asset itself. 3. Ds level of the indicators such as the life of plannel expenditure, the development of transportation and communication and the relationship between parents and children was highest, adn that of the use of leisure time, socialactivity, economic equality and social welfare was lowest. 4. Through the oblique rotation of Factor analysis, 12 factors were extracted (total eigen value 32.663, total variance 50.251%). Specially Factor 1 which was christened as the equality and development of social-economic life was related with 11 indicators and its common variance was 51.68%. 5. The rural residents, the lower income group and the lower educated group, who were told of being under disadvantage and unfair treatment of social-economic status, showed higher suffiency on the equity of income allocation, the freedom of living and expression, and judicial equality. 6. Generally speaking, the urban residents, the group under 39 years old, the higher educated group had more intensive Need of Living than the other groups.

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