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Mixture models detect large effect QTL better than GBLUP and result in more accurate and persistent predictions

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Journal of Animal Science and Biotechnology 2016年第4期7卷 468-473页

作者： Anna Wolc Jesus Arango Petek Settar Janet E.Fulton Neil P.O'Sullivan Jack C.M.Dekkers Rohan Fernando Dorian J.Garrick Department of Animal Science Iowa State University Hy-Line International Dallas Center

Background： Accurate evaluation of SNP effects is important for genome wide association studies and for genomic prediction. The genetic architecture of quantitative traits differs widely, with some traits exhibiting few if any quantitative trait loci （QTL） with large effects, while other traits have one or several easily detectable QTL with large effects. methods： Body weight in broilers and egg weight in layers are two examples of traits that have QTL of large effect A commonly used method for genome wide association studies is to fit a mixture model such as BayesB that assumes some known proportion of SNP effects are zero. In contrast, the most commonly used method for genomic prediction is known as GBLUP, which involves fitting an animal model to phenotypic data with the variance-covariance or genomic relationship matrix among the animals being determined by genome wide SNP genotypes. Genotypes at each SNP are typically weighted equally in determining the genomic relationship matrix for GBLUP. We used the equivalent marker effects model formulation of GBLUP for this study. We compare these two classes of models using egg weight data collected over 8 generations from 2,324 animals genotyped with a 42 K SNP ***： Using data from the first 7 generations, both BayesB and GBLUP found the largest QTL in a similar well-recognized QTL region, but this QTL was estimated to account for 24 % of genetic variation with BayesB and less than 1% with GBLUP. When predicting phenotypes in generation 8 BayesB accounted for 36 % of the phenotypic variation and GBLUP for 25 %. When using only data from any one generation, the same QTL was identified with BayesB in all but one generation but never with GBLUP. Predictions of phenotypes in generations 2 to 7 based on only 295 animals from generation 1 accounted for 10 % phenotypic variation with BayesB but only 6 % with GBLUP. Predicting phenotype using only the marker effects in the 1 Mb region that accounted for the largest e

关键词： bayesian methods Bias QTL

Ensembles vs. information theory: supporting science underuncertainty

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Frontiers of Earth Science 2018年第4期12卷 653-660页

作者： Grey S.NEARING Hoshin V.GUPTA University of Alabama Department of Geological SciencesTuscaloosaAL 35294-1152USA University of Arizona Department of Hydrology and Atmospheric SciencesTucsonAZ 85721-0158USA

Multi-model ensembles are one of the most common ways to deal with epistemic uncertainty in hydrology. This is a problem because there is no known way to sample models such that the resulting ensemble admits a measure that has any systematic (i.e., asymptotic, bounded, or consistent) relationship with uncertainty. Multi-model ensembles are effectively sensitivity analyses and cannot - even partially - quantify uncertainty. One consequence of this is that multi-model approaches cannot support a consistent scientific method - in particular, multi-model approaches yield unbounded errors in inference. In contrast, information theory supports a coherent hypothesis test that is robust to (i.e., bounded under) arbitrary epistemic uncertainty. This paper may be understood as advocating a procedure for hypothesis testing that does not require quantifying uncertainty, but is coherent and reliable (i.e., bounded) in the presence of arbitrary (unknown and unknowable) uncertainty. We conclude by offering some suggestions about how this proposed philosophy of science suggests new ways to conceptualize and construct simulation models of complex, dynamical systems.

关键词： information theory multi-model ensembles bayesian methods uncertainty quantification hypothesis testing

Comparative study of estimation methods for genomic breeding values

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Science Bulletin 2016年第5期61卷 353-356页

作者： Chonglong Wang Qin Zhang Li Jiang Rong Qian Xiangdong Ding Yaofeng Zhao Institute of Animal Husbandry and Veterinary Medicine AnhuiAcademy of Agricultural Sciences Key Laboratory of Animal Genetics Breeding and Reproduction Ministry of Agriculture of China College of Animal Science and Technology China Agricultural University College of Biological Sciences China Agricultural University

Estimation of genomic breeding values is important in genomic selection. bayesian and BLUP methods are the main techniques employed. In this study,we conducted a comparative study of Bayes A, Bayes B,Bayes Cp and GBLUP methods in simulated data and real data of Chinese Holstein cattle. Results showed that, in simulated data, the accuracies of all methods were all similarly elevated with the increase of reference population size, but they made different responses to the changes of marker number or QTL number. In real data of Chinese Holstein cattle, Bayes A generated the highest accuracy almost for all six traits, and GBLUP performed as well as Bayes A for the traits of milk yield, fat yield and protein yield, while for the trait of fat percentage, protein percentage and somatic cell score, three bayesian methods showed superior to GBLUP. Comprehensively analyzing above results, it can be speculated that accuracies of the three bayesian methods are not only influenced by the absolute value of QTL number or marker number, but may also be influenced by the ratio of QTL number to marker number. And there is at least one kind of bayesian methods performing better than GBLUP, when the ratio of QTL number versus marker number is very small or involving large-effect QTL.

关键词： Genomic selection Estimation of genomic breeding values bayesian methods BLUP methods

A system for determining maximum tolerated dose in clinical trial

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Statistical Theory and Related Fields 2021年第4期5卷 288-302页

作者： Keying Ye Xiaobin Yang Ying Ji Min Wang University of Texas at San Antonio San AntonioTXUSA HouseCanary Inc. San AntonioTXUSA

Toxicity study,especially in determining the maximum tolerated dose(MTD)in phase I clinical trial,is an important step in developing new life-saving *** practice,toxicity levels may be categorised as binary grades,multiple grades,or in a more generalised case,continuous *** this study,we propose an overall MTD framework that includes all the aforementioned cases for a single toxicity outcome(response).The mechanism of determining MTD involves a function that is predetermined by *** properties of such a system are investigated and simu-lation studies are performed for various *** concept of the continual reassessment method(CRM)is also implied in the framework and bayesian analysis,including Markov chain Monte Carlo(MCMC)methods are used in estimating the model parameters.

关键词： bayesian methods continual reassessment method(CRM) drug dose finding Markov chain Monte Carlo(MCMC)method maximum tolerated dose(MTD) phase I clinical trial toxicity responses

A Regularized Minimum Cross-entropy Algorithm on Mixture of Experts for Curve Detection

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A Regularized Minimum Cross-entropy Algorithm on Mixture of ...

Modelling Distribution of Under-Five Child Diarrhoea across Malawi

2005 International Conference on Neural Networks and Brain(ICNN&B’05)

作者： Zhiwu Lu Institute of Computer Science ＆ Technology of Peking University

ISBN: (纸本)0780394224

＜正＞Curve detection is a basic problem in image processing and remains a difficult *** this paper,with the help of regularization theory,we aim to solve this problem via a gradient regularized minimum cross-entropy（RMCE） algorithm on the mixture of experts（ME） model,which can automatically make model *** is demonstrated by the simulation and image experiments that this gradient algorithm can not only detect curves（straight lines or circles） against noise,but also automatically determine the number of curves during parameter learning.

关键词： Image processing Computer science Computer vision Pattern recognition Sequential analysis Pixel bayesian methods Equations

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Journal of Mathematics and System Science 2016年第3期6卷 91-101页

作者： Tsirizani M. Kaombe Jimmy J. Namangale Mathematical Sciences Department Chancellor College - University of Malawi P.O. Box 280 Zomba Malawi

Analysis of diarrhoea data in Malawi has been commonly done using classical methods. However, different approaches, such as bayesian methods, have been introduced in literature. This study aimed at trying out semi-parametric methods in comparison with classical ones, as well as how each isolates risk factors for child diarrhoea. This was done by fitting Logit, Poisson, and bayesian models to 2006 Malawi Multiple Indicator Cluster Survey data. The comparison between Logit and Poisson models was done via chi-square＇s goodness-of-fit test. Confidence and Credible Intervals were used to compare Logit/Poisson and bayesian model estimates. Modelling and inference in bayesian method was done through MCMC techniques. The results showed agreement in significance and direction of estimates from bayesian and Poisson/Logit models, but Poisson provided better fit than Logit model. Further, all the models identified child＇s age, breastfeeding status, region of stay and toilet-sharing status as significant factors for determining the child＇s risk. The models ruled out effects of mother＇s education, area of residence, and source of drinking water on the risk. bayesian model separately proved significant closeness to lake/river factor. The findings imply that classical and semi-parametric models are equally helpful when estimating the child＇s risk to diarrhoea.

关键词： Child Diarrhoea bayesian methods Logit model Poisson model.

Load Forecasting for Control of the Use of Transmission System for Electric Distribution Utilities

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Journal of Energy and Power Engineering 2013年第1期7卷 139-147页

作者： Vitor Hugo Ferreira Alexandre Rasi Aoki Silvio Michel de Rocco Electrical Engineering Department Fluminense Federal University (UFF) Niter6i 24210-240 Brazil Electric and Electronic Department Institute of Technology for Development (LACTEC) Curitiba 80215-090 Brazil Companhia Paranaense de Energia (COPEL) Curitiba 80420-170 Brazil

The Brazilian electric sector reform established that the remuneration of distribution utilities must be through the management of their systems. This fact increased the necessity of control and management of load flows through the connection points between the distribution systems and the basic grid as a function of the contracted amounts. The objective of this control is to avoid that these flows exceed some thresholds along the contracted values, avoiding monetary penalties to the utility or unnecessary amounts of contracted flows that overrates the costumers. This question highlights the necessity of forecast the flows in these connection points in sufficient time to permit the operator to take decisions to avoid flows beyond the contracted ones. In this context, this work presents the development of a neural network based load flow forecaster, being tested two time-series neural models： support vector machines and bayesian inference applied to multilayered perceptron. The models are applied to real data from a Brazilian distribution utility.

关键词： Load forecasting artificial neural networks complexity control input selection bayesian methods support vector machines.

Precipitation Regime Shift Associated with the Pacific Decadal Oscillation in the Maritime Continent

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American Journal of Climate Change 2020年第2期9卷 123-135页

作者： V. Kokorev J. Ettema P. Siegmund G. van der Schrier Faculty of Geo-Information Science and Earth Observation University of Twente Enschede The Netherlands Royal Netherlands Meteorological Institute (KNMI) De Bilt The Netherlands

Recent changes in precipitation regime in South-East Asia are a subject of ongoing discussion. In this article, for the first time, evidence of a precipitation regime shift during the mid-1970s in the Northern Hemispheric part of South-East Asia is demonstrated. The detection of regime shifts is made possible by using a new comprehensive dataset of daily precipitation records (South-East Asian Climate Assessment and Dataset) and applying a novel bayesian approach for regime shift detection. After the detected regime shift event in the mid-1970s, significant changes in precipitation distribution occurred in the Northern Hemispheric regions—Indochina Peninsula and the Philippines. More specifically, dry days became up to 10% more frequent in some regions. However, no precipitation regime shift is detected in Southern Hemisphere regions—Java and Northern Australia, were the number of observed dry days increased gradually.

关键词： Regime Shift Pacific Decadal Oscillation Precipitation South-East Asia bayesian methods

A Robust Iterative Multiframe Super-Resolution Reconstruction using a bayesian Approach with Tukey’s Biweigth

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A Robust Iterative Multiframe Super-Resolution Reconstructio...