Добірка наукової літератури з теми "Best Linear Unbiased Prediction (BLUP)"
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Статті в журналах з теми "Best Linear Unbiased Prediction (BLUP)"
Bai, Chao, and Haiqi Li. "Simultaneous prediction in the generalized linear model." Open Mathematics 16, no. 1 (August 24, 2018): 1037–47. http://dx.doi.org/10.1515/math-2018-0087.
Повний текст джерелаVIANA, J. M. S., G. B. MUNDIM, R. O. DELIMA, F. F. E SILVA, and M. D. V. DE RESENDE. "Best linear unbiased prediction for genetic evaluation in reciprocal recurrent selection with popcorn populations." Journal of Agricultural Science 152, no. 3 (May 23, 2013): 428–38. http://dx.doi.org/10.1017/s0021859613000270.
Повний текст джерелаMarcelo Soriano Viana, José, Vinícius Ribeiro Faria, Fabyano Fonseca e Silva, and Marcos Deon Vilela de Resende. "Combined selection of progeny in crop breeding using best linear unbiased prediction." Canadian Journal of Plant Science 92, no. 3 (May 2012): 553–62. http://dx.doi.org/10.4141/cjps2011-110.
Повний текст джерелаGrundy, B., and WG Hill. "A method for reducing inbreeding with Best Linear Unbiased Prediction." Proceedings of the British Society of Animal Production (1972) 1993 (March 1993): 33. http://dx.doi.org/10.1017/s030822960002362x.
Повний текст джерелаNOVIKOV, A. A., E. N. SUSLINA, G. S. POKHODNYA, D. G. SHIСHKIN, YA A. KHABIBRAKHMANOVA, and N. V. BASHMAKOVA. "SELECTION OF SOWS BY GENETIC MARKERS AND BLUP INDEX." Izvestiâ Timirâzevskoj selʹskohozâjstvennoj akademii, no. 4 (2021): 94–107. http://dx.doi.org/10.26897/0021-342x-2021-4-94-107.
Повний текст джерелаKlápště, J., M. Lstibůrek, and J. Kobliha. "Initial evaluation of half-sib progenies of Norway spruce using the best linear unbiased prediction." Journal of Forest Science 53, No. 2 (January 7, 2008): 41–46. http://dx.doi.org/10.17221/2136-jfs.
Повний текст джерелаFORKMAN, J., and H.-P. PIEPHO. "Performance of empirical BLUP and Bayesian prediction in small randomized complete block experiments." Journal of Agricultural Science 151, no. 3 (May 16, 2012): 381–95. http://dx.doi.org/10.1017/s0021859612000445.
Повний текст джерелаXiang, Bin, and Bailian Li. "A new mixed analytical method for genetic analysis of diallel data." Canadian Journal of Forest Research 31, no. 12 (December 1, 2001): 2252–59. http://dx.doi.org/10.1139/x01-154.
Повний текст джерелаPRAJAPATI, B. M., J. P. GUPTA, J. D. CHAUDHARI, G. A. PARMAR, R. N. SATHWARA, H. H. PANCHASARA, P. A. PATEL, and M. N. PRAJAPATI. "Utility of first lactation fat energy corrected milk yield as a trait for genetic evaluation of Mehsana buffalo bulls using various sire evaluation methods." Indian Journal of Animal Sciences 90, no. 2 (March 6, 2020): 259–63. http://dx.doi.org/10.56093/ijans.v90i2.98821.
Повний текст джерелаBijma, Piter, and John A. Woolliams. "Prediction of Rates of Inbreeding in Populations Selected on Best Linear Unbiased Prediction of Breeding Value." Genetics 156, no. 1 (September 1, 2000): 361–73. http://dx.doi.org/10.1093/genetics/156.1.361.
Повний текст джерелаДисертації з теми "Best Linear Unbiased Prediction (BLUP)"
Hettasch, Marianne Helena. "Applicability of best linear unbiased prediction (BLUP) for the selection of ortets in Eucalyptus hybrid populations." Diss., Pretoria : [s.n.], 2009. http://upetd.up.ac.za/thesis/available/etd-08062009-122539.
Повний текст джерелаEatwell, Karen Anne. "Remediation of instability in Best Linear Unbiased Prediction." Thesis, University of Pretoria, 2013. http://hdl.handle.net/2263/40245.
Повний текст джерелаThesis (PhD)--University of Pretoria, 2013.
gm2014
Genetics
unrestricted
Li, Huilin. "Small area estimation an empirical best linear unbiased prediction approach /." College Park, Md.: University of Maryland, 2007. http://hdl.handle.net/1903/7600.
Повний текст джерелаThesis research directed by: Mathematical Statistics Program. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
Ladejobi, Olufunmilayo Olubukola. "Testing new genetic and genomic approaches for trait mapping and prediction in wheat (Triticum aestivum) and rice (Oryza spp)." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/277449.
Повний текст джерелаMbah, Alfred Kubong. "On the theory of records and applications." [Tampa, Fla.] : University of South Florida, 2007. http://purl.fcla.edu/usf/dc/et/SFE0002216.
Повний текст джерела(13991187), Joseph W. Daley. "Mixed model methods for quantitative trait loci estimation in crosses between outbred lines." Thesis, 2003. https://figshare.com/articles/thesis/Mixed_model_methods_for_quantitative_trait_loci_estimation_in_crosses_between_outbred_lines/21376767.
Повний текст джерелаMethodology is developed for Quantitative Trait Loci (QTL) analysis in F2 and backcross designed experiments between outbred lines using a mixed model framework through the modification of segment mapping techniques. Alleles are modelled in the F1 and parental generations allowing the estimation of individual additive allele effects while accounting for QTL segregation within lines as well as differences in mean QTL effects between lines.
Initially the theory, called F1 origin mapping, is developed for a single trait scenario involving possible multiple QTL and polygenic variation. Additive genetic variances are estimated via Restricted Maximum Likelihood (REML) and allele effects are modelled using Best Linear Unbiased Prediction (BLUP). Simulation studies are carried out comparing F1 origin mapping with existing segment mapping methods in a number of genetic scenarios. While there was no significant difference in the estimation of effects between the two methods the average CPU time of one hundred replicates was 0.26 seconds for F1 origin mapping and 3.77 seconds for the segment mapping method. This improvement in computation efficiency is due to the restructuring of IBD matrices which result in the inversion and REML iteration over much smaller matrices.
Further theory is developed which extends F1 origin mapping from single to multiple trait scenarios for F2 crosses between outbred lines. A bivariate trait is simulated using a single QTL with and without a polygenic component. A single trait and bivariate trait analysis are performed to compare the two approaches. There was no significant difference in the estimation of QTL effects between the two approaches. However, there was a slight improvement in the accuracy of QTL position estimates in the multiple trait approach. The advantage of F1 origin mapping with regard to computational efficiency becomes even more important with multiple trait analysis and allows the investigation of interesting biological models of gene expression.
F1 origin mapping is developed further to model the correlation structure inherent in repeated measures data collected on F2 crosses between outbred lines. A study was conducted to show that repeated measures F1 origin mapping and multiple trait F1 origin mapping give similar results in certain circumstances. Another simulation study was also conducted in which five regular repeated measures where simulated with allele breed difference effects and allele variances increasing linearly over time. Various polynomial orders of fit where investigated with the linear order of fit most parsimoniously modelling the data. The linear order of fit correctly identified the increasing trend in both the additive allele difference and allele variance. Repeated measures F1 origin mapping possesses the benefits of using the correlated nature of repeated measures while increasing the efficiency of QTL parameter estimation. Hence, it would be useful for QTL studies on measurements such as milk yield or live weights when collected at irregular intervals.
Theory is developed to combine the data from QTL studies involving F2 and backcross designed experiments. Genetic covariance matrices are developed for random QTL effects by modelling allele variation in the parental generation instead of the offspring generation for an F2 and backcross between outbred lines. The result is a general QTL estimation method called parental origin mapping. Phenotypes and genotypes from such a study involving Romney and Merino sheep are analysed providing evidence for a QTL affecting adult and hogget fibre diameter.
By coupling these new methods with computer software programs such as ASREML, F1 origin mapping and parental origin mapping provide powerful and flexible tools for QTL studies with the ability to efficiently handle single traits, multiple traits and repeated measures.
Tung, Ya-Chen, and 童雅禎. "Hybrid Wavelet Shrinkage Based on Best Linear Unbiased Prediction." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/71174552705507690053.
Повний текст джерела國立交通大學
統計學系
85
Nonparametric regression problems can be modeled as nonparmetric mixed-effects models. The Gauss-Markov theorems of nonparametric mixed-effects models suggest the best linear unbiased prediction(BLUP) (Huang and Lu,1997). Owing to the spatial adaptivity and multiresolution analysis property, wavelets can be useful basis to represent complicated signal variability. This report will generalize the perspective of BLUP on the wavelet representation. It suggests new shrinkage methods to denoise the observed signals efficiently as demonstrated in this report. Shrinkage parameters are estimated by the data automatically in speed. The resulting adaptive wavelet shrinkage methods can reconstrct signals with small average square errors without increasing the computational cost. These methods also work for arbitary designs, including the fixed dyadic designs.
Частини книг з теми "Best Linear Unbiased Prediction (BLUP)"
Montesinos López, Osval Antonio, Abelardo Montesinos López, and Jose Crossa. "Preprocessing Tools for Data Preparation." In Multivariate Statistical Machine Learning Methods for Genomic Prediction, 35–70. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89010-0_2.
Повний текст джерелаHarville, D. A. "BLUP (Best Linear Unbiased Prediction) and Beyond." In Advances in Statistical Methods for Genetic Improvement of Livestock, 239–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-74487-7_12.
Повний текст джерелаZimmerman, Dale L. "Best Linear Unbiased Prediction." In Linear Model Theory, 301–39. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52063-2_13.
Повний текст джерелаZimmerman, Dale L. "Best Linear Unbiased Prediction." In Linear Model Theory, 185–222. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52074-8_13.
Повний текст джерелаBalakrishnan, N., and William W. S. Chen. "Best Linear Unbiased Prediction." In Handbook of Tables for Order Statistics from Lognormal Distributions with Applications, 31–38. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5309-0_6.
Повний текст джерелаShekhar, Shashi, and Hui Xiong. "Best Linear Unbiased Prediction." In Encyclopedia of GIS, 52. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_98.
Повний текст джерелаWhite, Timothy L., and Gary R. Hodge. "Best Linear Unbiased Prediction: Introduction." In Predicting Breeding Values with Applications in Forest Tree Improvement, 276–98. Dordrecht: Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-015-7833-2_11.
Повний текст джерелаWhite, Timothy L., and Gary R. Hodge. "Best Linear Unbiased Prediction: Applications." In Predicting Breeding Values with Applications in Forest Tree Improvement, 300–327. Dordrecht: Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-015-7833-2_12.
Повний текст джерелаXu, Shizhong. "Selection Index and the Best Linear Unbiased Prediction." In Quantitative Genetics, 265–82. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-83940-6_16.
Повний текст джерелаSantner, Thomas J., Brian J. Williams, and William I. Notz. "Empirical Best Linear Unbiased Prediction of Computer Simulator Output." In Springer Series in Statistics, 67–114. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-8847-1_3.
Повний текст джерелаТези доповідей конференцій з теми "Best Linear Unbiased Prediction (BLUP)"
Salma, Admi, Kusman Sadik, and Khairil Anwar Notodiputro. "Small area estimation of per capita expenditures using robust empirical best linear unbiased prediction (REBLUP)." In STATISTICS AND ITS APPLICATIONS: Proceedings of the 2nd International Conference on Applied Statistics (ICAS II), 2016. Author(s), 2017. http://dx.doi.org/10.1063/1.4979443.
Повний текст джерелаSunandi, Etis, Dian Agustina, and Herlin Fransiska. "Estimating the Poverty level in the Coastal Areas of Mukomuko District Using Small Area Estimation: Empirical Best Linear Unbiased Prediction Method." In Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia. EAI, 2020. http://dx.doi.org/10.4108/eai.2-8-2019.2290477.
Повний текст джерелаAminah, Agustin Siti, Gandhi Pawitan, and Bertho Tantular. "Empirical best linear unbiased prediction method for small areas with restricted maximum likelihood and bootstrap procedure to estimate the average of household expenditure per capita in Banjar Regency." In STATISTICS AND ITS APPLICATIONS: Proceedings of the 2nd International Conference on Applied Statistics (ICAS II), 2016. Author(s), 2017. http://dx.doi.org/10.1063/1.4979426.
Повний текст джерелаOtchere, Daniel Asante, David Hodgetts, Tarek Arbi Omar Ganat, Najeeb Ullah, and Alidu Rashid. "Static Reservoir Modeling Comparing Inverse Distance Weighting to Kriging Interpolation Algorithm in Volumetric Estimation. Case Study: Gullfaks Field." In Offshore Technology Conference. OTC, 2021. http://dx.doi.org/10.4043/30919-ms.
Повний текст джерелаЗвіти організацій з теми "Best Linear Unbiased Prediction (BLUP)"
Weller, Joel I., Ignacy Misztal, and Micha Ron. Optimization of methodology for genomic selection of moderate and large dairy cattle populations. United States Department of Agriculture, March 2015. http://dx.doi.org/10.32747/2015.7594404.bard.
Повний текст джерела