Relevant bibliographies by topics / Best Linear Unbiased Prediction (BLUP)

Academic literature on the topic 'Best Linear Unbiased Prediction (BLUP)'

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Published: 10 December 2022
Last updated: 19 February 2023

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Journal articles on the topic "Best Linear Unbiased Prediction (BLUP)"

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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.

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AbstractThis paper studies the prediction based on a composite target function that allows to simultaneously predict the actual and the mean values of the unobserved regressand in the generalized linear model. The best linear unbiased prediction (BLUP) of the target function is derived. Studies show that our BLUP has better properties than some other predictions. Simulations confirm its better finite sample performance.
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VIANA, 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.

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SUMMARYThe objective of the present study was to present the theory and application of best linear unbiased prediction (BLUP) in reciprocal recurrent selection (RRS). Seven progeny tests from two RRS programmes with popcorn (Zea mays L. ssp. mays [syn. Zea mays L. ssp. everta (Sturtev.) Zhuk.]) populations were conducted and analysed for expansion volume and grain yield. The interpopulation half- and full-sib family models were fitted using ASReml software. Half-sib selection is equivalent to selection for the general combining ability (GCA) of the common parents. With inbred full-sib progeny and BLUP analysis, it is possible to predict the general and specific combining ability effects. The standard error of prediction of the progeny effect was lower than the standard deviation of the best linear unbiased estimation (BLUE) estimate. For half- and full-sib RRS, the BLUE and BLUP provided highly correlated estimates of progeny genotypic values. The coincidence between selected parents ranged from 64 to 95%. With inbred full-sib progeny, the correlations between the BLUE of progeny genotypic values and the BLUP of GCA effects were lower. Consequently, the coincidence between selected parents was lower, ranging from 0 to 57%. The percentage of common selected inbred progeny based on the BLUE and BLUP of the progeny genotypic value ranged from 57 to 100%.
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Marcelo 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.

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Viana, J. M. S., Faria, V. R., Fonseca e Silva, F. and Vilela de Resende, M. D. 2012. Combined selection of progeny in crop breeding using best linear unbiased prediction. Can. J. Plant Sci. 92: 553–562. Combined selection is an important strategy in crop breeding. As the classical index does not consider pedigree information, the objective of this study was to evaluate the efficiency of the best linear unbiased prediction (BLUP) methodology for combined selection of progeny. We analyzed expansion volume (EV) and grain yield of parents and inbred and non-inbred progeny from the popcorn population Viçosa. The BLUP analyses, single-trait and of the same character measured in parents and progeny (combined parent-family) were performed using the ASReml software. Because the experiments were balanced, the estimates of the additive variance from the BLUP and least squares analyses were generally equivalent. The accuracies of the BLUP analyses do not clearly establish the superior technique. The accuracy of the classical index tended to be higher than that obtained from BLUP analyses. There was equivalence between BLUP and least squares analyses relative to half-sib and inbred progeny selection, and superiority of the combined parent-family BLUP index for full-sib selection. The BLUP analyses also differed from the least squares analysis on the coincidence of selected parents. The populations obtained by selection based on BLUP of breeding values presented a lower effective size.
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Grundy, 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.

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An optimum way of selecting animals is through a prediction of their genetic merit (estimated breeding value, EBV), which can be achieved using a best linear unbiased predictor (BLUP) (Henderson, 1975). Selection decisions in a commercial environment, however, are rarely made solely on genetic merit but also on additional factors, an important example of which is to limit the accumulation of inbreeding. Comparison of rates of inbreeding under BLUP for a range of hentabilities highlights a trend of increasing inbreeding with decreasing heritability. It is therefore proposed that selection using a heritability which is artificially raised would yield lower rates of inbreeding than would otherwise be the case.
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NOVIKOV, 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.

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The authors conducted studies on the effect of the estrogen receptor (ESR), prolactin receptor (PRLR), and ryanodine receptor (RYR-1) genotypes on the breeding value of sows. Using the BLUP method, they evaluated the indicators of the large white, landrace, and Duroc breeds to develop a regional hybridization system in the pig industry of the Belgorod region. The research determined a significant influence of the “desirable” BB and AB genotypes of the ESR gene in large white sows and the “desirable” BB genotype of the RPLR gene in Landrace and Duroc sows on the maternal BLUP index.
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Klápště, 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.

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The present paper deals with data obtained from fifteen years old Norway spruce (<i>Picea abies</i> [L.] Karst.) progeny test established at three sites in the Sázava River region. Parameter under the evaluation was a tree height in 15 years following the establishment of the trial. Genetic parameters were estimated using the REML (Restricted Maximum Likelihood) procedure followed by the BLUP (Best Linear Unbiased Prediction). Genetic parameters estimates were used to predict genetic gain in three alternative selection strategies. The value of gain depends on target value of gene diversity. 10&minus;15% gain is due to selecting breeding population composed of 50 individuals. Based on these quantitative findings, current and future research orientation is discussed.
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FORKMAN, 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.

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SUMMARYThe model for analysis of randomized complete block (RCB) experiments usually includes two factors: block and treatment. If treatment is modelled as fixed, best linear unbiased estimation (BLUE) is used, and treatment means estimate expected means. If treatment is modelled as random, best linear unbiased prediction (BLUP) shrinks the treatment means towards the overall mean, which results in smaller root-mean-square error (RMSE) in prediction of means. This theoretical result holds provided the variance components are known, but in practice the variance components are estimated. BLUP using estimated variance components is called empirical best linear unbiased prediction (EBLUP). In small experiments, estimates can be unreliable and the usefulness of EBLUP is uncertain. The present paper investigates, through simulation, the performance of EBLUP in small RCB experiments with normally as well as non-normally distributed random effects. The methods of Satterthwaite (1946) and of Kenward & Roger (1997, 2009), as implemented in the SAS System, were studied. Performance was measured by RMSE, in prediction of means, and coverage of prediction intervals. In addition, a Bayesian approach was used for prediction of treatment differences and computation of credible intervals. EBLUP performed better than BLUE with regard to RMSE, also when the number of treatments was small and when the treatment effects were non-normally distributed. The methods of Satterthwaite and of Kenward & Roger usually produced approximately correct coverage of prediction intervals. The Bayesian method gave the smallest RMSE and usually more accurate coverage of intervals than the other methods.
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Xiang, 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.

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Diallel is a popular mating design used for crop and tree breeding programs, but its unique feature of a single observation with two levels of the same main effect, general combining ability (GCA), makes it difficult to analyze with standard statistical programs. A new approach using the SAS PROC MIXED is developed in this study for analyzing genetic data from diallel mating. Dummy variables for GCA effects were first constructed with SAS PROC IML, then PROC MIXED procedure was used to estimate variance components and to obtain BLUE (best linear unbiased estimators) of fixed effects and BLUP (best linear unbiased predictors) of random genetic effects (GCA and specific combining ability (SCA) effects) simultaneously. The new method can also be used for predicting individual breeding values with BLUP methodology, applying SAS IML to the outputs provided by PROC MIXED to calculate breeding value for each individual in the progeny test, adjusted for the fixed effects such as test location. The accurate BLUP prediction, the ability to estimate individual breeding values, and the ease of use would make this new method especially attractive for analyzing tree breeding data.
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PRAJAPATI, 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.

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India being a vegetarian country, milk is the major source of dietary bio-energy, but majority of animals are routinely being evaluated on the basis of their milk producing ability. The present study was aimed to come up with a sire evaluation methodology based on first lactation Fat Energy Corrected Milk Yield (FBE) in order to obtain an accurate and unbiased estimate of breeding value of Mehsana buffalo bulls and ranking them on the basis of their daughter's performance for future herd improvement. Data for the present study included 7825 she buffaloes in their first lactations, extended over a period of 25 years (1989 to 2013), from field progeny testing programme of Dudhsagar Research and Development Association (DURDA), Dudhsagar Dairy, Mehsana, Gujarat. The data were classified into different subclasses based on period, season, cluster and age at first calving group. The average breeding values of Mehsana buffalo bulls evaluated for FBE by least squares method (LSM), best linear unbiased prediction sire model (BLUP-SM) and best linear unbiased prediction animal model (BLUP-AM) methods were 1215.89, 1185.7 and 1185.7 kcal, respectively. BLUP-AM method had lowest error variance as compared to LSM and BLUP-SM methods of sire evaluation. This indicated that BLUP-AM was most efficient method.
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Bijma, 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.

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Abstract Predictions for the rate of inbreeding (ΔF) in populations with discrete generations undergoing selection on best linear unbiased prediction (BLUP) of breeding value were developed. Predictions were based on the concept of long-term genetic contributions using a recently established relationship between expected contributions and rates of inbreeding and a known procedure for predicting expected contributions. Expected contributions of individuals were predicted using a linear model, μi(x) = α βsi, where si denotes the selective advantage as a deviation from the contemporaries, which was the sum of the breeding values of the individual and the breeding values of its mates. The accuracy of predictions was evaluated for a wide range of population and genetic parameters. Accurate predictions were obtained for populations of 5–20 sires. For 20–80 sires, systematic underprediction of on average 11% was found, which was shown to be related to the goodness of fit of the linear model. Using simulation, it was shown that a quadratic model would give accurate predictions for those schemes. Furthermore, it was shown that, contrary to random selection, ΔF less than halved when the number of parents was doubled and that in specific cases ΔF may increase with the number of dams.
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Dissertations / Theses on the topic "Best Linear Unbiased Prediction (BLUP)"

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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.

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Eatwell, Karen Anne. "Remediation of instability in Best Linear Unbiased Prediction." Thesis, University of Pretoria, 2013. http://hdl.handle.net/2263/40245.

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In most breeding programmes breeders use phenotypic data obtained in breeding trials to rank the performance of the parents or progeny on pre-selected performance criteria. Through this ranking the best candidates are identified and selected for breeding or production purposes. Best Linear Unbiased Prediction (BLUP), is an efficient selection method to use, combining information into a single index. Unbalanced or messy data is frequently found in tree breeding trial data. Trial individuals are related and a degree of correlation is expected between individuals over sites, which can lead to collinearity in the data which may lead to instability in certain selection models. A high degree of collinearity may cause problems and adversely affect the prediction of the breeding values in a BLUP selection index. Simulation studies have highlighted that instability is a concern and needs to be investigated in experimental data. The occurrence of instability, relating to collinearity, in BLUP of tree breeding data and possible methods to deal with it were investigated in this study. Case study data from 39 forestry breeding trials (three generations) of Eucalyptus grandis and 20 trials of Pinus patula (two generations) were used. A series of BLUP predictions (rankings) using three selection traits and 10 economic weighting sets were made. Backward and forward prediction models with three different matrix inversion techniques (singular value decomposition, Gaussian elimination - partial and full pivoting) and an adapted ridge regression technique were used in calculating BLUP indices. A Delphi and Clipper version of the same BLUP programme which run with different computational numerical precision were used and compared. Predicted breeding values (forward prediction) were determined in the F1 and F2 E. grandis trials and F1 P. patula trials and realised breeding performance (backward prediction) was determined in the F2 and F3 E. grandis trials and F2 P. patula trials. The accuracy (correlation between the predicted breeding values and realised breeding performance) was estimated in order to assess the efficiency of the predictions and evaluate the different matrix inversion methods. The magnitude of the accuracy (correlations) was found to mostly be of acceptable magnitude when compared to the heritability of the compound weighted trait in the F1F2 E. grandis scenarios. Realised genetic gains were also calculated for each method used. Instability was observed in both E. grandis and P. patula breeding data in the study, and this may cause a significant loss in realised genetic gains. Instability can be identified by examining the matrix calculated from the product of the phenotypic covariance matrix with its inverse, for deviations from the expected identity pattern. Results of this study indicate that it may not always be optimal to use a higher numerical precision programme when there is collinearity in the data and instability in the matrix calculations. In some cases, where there is a large amount of collinearity, the use of a higher precision programme for BLUP calculations can significantly increase or decrease the accuracy of the rankings. The different matrix inversion techniques particularly SVD and adapted ridge regression did not perform much better than the full pivoting technique. The study found that it is beneficial to use the full pivoting Gaussian elimination matrix inversion technique in preference to the partial pivoting Gaussian elimination matrix inversion technique for both high and lower numerical precision programmes.
Thesis (PhD)--University of Pretoria, 2013.
gm2014
Genetics
unrestricted
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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.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2007.
Thesis 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.
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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.

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Advances in molecular marker technologies have led to the development of high throughput genotyping techniques such as Genotyping by Sequencing (GBS), driving the application of genomics in crop research and breeding. They have also supported the use of novel mapping approaches, including Multi-parent Advanced Generation Inter-Cross (MAGIC) populations which have increased precision in identifying markers to inform plant breeding practices. In the first part of this thesis, a high density physical map derived from GBS was used to identify QTLs controlling key agronomic traits of wheat in a genome-wide association study (GWAS) and to demonstrate the practicability of genomic selection for predicting the trait values. The results from GBS were compared to a previous study conducted on the same association mapping panel using a less dense physical map derived from diversity arrays technology (DArT) markers. GBS detected more QTLs than DArT markers although some of the QTLs were detected by DArT markers alone. Prediction accuracies from the two marker platforms were mostly similar and largely dependent on trait genetic architecture. The second part of this thesis focused on MAGIC populations, which incorporate diversity and novel allelic combinations from several generations of recombination. Pedigrees representing a wild rice MAGIC population were used to model MAGIC populations by simulation to assess the level of recombination and creation of novel haplotypes. The wild rice species are an important reservoir of beneficial genes that have been variously introgressed into rice varieties using bi-parental population approaches. The level of recombination was found to be highly dependent on the number of crosses made and on the resulting population size. Creation of MAGIC populations require adequate planning in order to make sufficient number of crosses that capture optimal haplotype diversity. The third part of the thesis considers models that have been proposed for genomic prediction. The ridge regression best linear unbiased prediction (RR-BLUP) is based on the assumption that all genotyped molecular markers make equal contributions to the variations of a phenotype. Information from underlying candidate molecular markers are however of greater significance and can be used to improve the accuracy of prediction. Here, an existing Differentially Penalized Regression (DiPR) model which uses modifications to a standard RR-BLUP package and allows two or more marker sets from different platforms to be independently weighted was used. The DiPR model performed better than single or combined marker sets for predicting most of the traits both in a MAGIC population and an association mapping panel. Overall the work presented in this thesis shows that while these techniques have great promise, they should be carefully evaluated before introduction into breeding programmes.
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Mbah, Alfred Kubong. "On the theory of records and applications." [Tampa, Fla.] : University of South Florida, 2007. http://purl.fcla.edu/usf/dc/et/SFE0002216.

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(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.

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Methodology 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.

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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.

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碩士
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.
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Book chapters on the topic "Best Linear Unbiased Prediction (BLUP)"

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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.

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AbstractThis data preparation chapter is of paramount importance for implementing statistical machine learning methods for genomic selection. We present the basic linear mixed model that gives rise to BLUE and BLUP and explain how to decide when to use fixed or random effects that give rise to best linear unbiased estimates (BLUE or BLUEs) and best linear unbiased predictors (BLUP or BLUPs). The R codes for fitting linear mixed model for the data are given in small examples. We emphasize tools for computing BLUEs and BLUPs for many linear combinations of interest in genomic-enabled prediction and plant breeding. We present tools for cleaning, imputing, and detecting minor and major allele frequency computation, marker recodification, frequency of heterogeneous, frequency of NAs, and three methods for computing the genomic relationship matrix. In addition, scaling and data compression of inputs are important in statistical machine learning. For a more extensive description of linear mixed models, see Chap. 10.1007/978-3-030-89010-0_5.
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Harville, 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.

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Zimmerman, 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.

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Zimmerman, 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.

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Balakrishnan, 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.

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Shekhar, 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.

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White, 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.

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White, 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.

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Xu, 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.

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Santner, 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.

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Conference papers on the topic "Best Linear Unbiased Prediction (BLUP)"

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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.

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Sunandi, 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.

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Aminah, 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.

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Otchere, 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.

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Abstract Understanding and characterizing the behaviour of the subsurface by combining it with a suitable statistical method gives a higher level of confidence in the reservoir model produced. Interpolation of porosity and permeability data with minimum error and high accuracy is, therefore, essential in reservoir modeling. The most widely used interpolation algorithm, kriging, with enough well data is the best linear unbiased estimator. This research sought to compare the applicability and competitiveness of inverse distance weighting (IDW) method using power index of 1, 2 and 4 to kriging when there is sparse data, due to time and budget constraints, to calculate hydrocarbon volumes in a fluvial-deltaic reservoir. Interpolation results, estimated from descriptive statistics, were insignificant and showed similar prediction accuracy and consistency but IDW with power index of 1 indicated the least error estimation and higher accuracy. The assessment of hydrocarbon volume calculations also showed a marginal difference below 0.08 between IDW power index of 1 and kriging in the reservoir zones. Reservoir segments cross-validation and correlation analysis results indicate IDW to have no significant difference to kriging with absolute errors of 3% for recoverable oil and 0.7% for recoverable gas. Grid upscaling, which usually causes a loss of geological features and extreme porosity values, did not impact the results but rather complemented the robustness of IDW in both fine and coarse grid upscale. With IDW exhibiting least errors and higher accuracy, the volumetric and statistical results confirm that when there are fewer well data in a fluvial-deltaic reservoir, the suitable spatial interpolation choice should be IDW method with a power index of 1.
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Reports on the topic "Best Linear Unbiased Prediction (BLUP)"

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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.

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The main objectives of this research was to detect the specific polymorphisms responsible for observed quantitative trait loci and develop optimal strategies for genomic evaluations and selection for moderate (Israel) and large (US) dairy cattle populations. A joint evaluation using all phenotypic, pedigree, and genomic data is the optimal strategy. The specific objectives were: 1) to apply strategies for determination of the causative polymorphisms based on the “a posteriori granddaughter design” (APGD), 2) to develop methods to derive unbiased estimates of gene effects derived from SNP chips analyses, 3) to derive optimal single-stage methods to estimate breeding values of animals based on marker, phenotypic and pedigree data, 4) to extend these methods to multi-trait genetic evaluations and 5) to evaluate the results of long-term genomic selection, as compared to traditional selection. Nearly all of these objectives were met. The major achievements were: The APGD and the modified granddaughter designs were applied to the US Holstein population, and regions harboring segregating quantitative trait loci (QTL) were identified for all economic traits of interest. The APGD was able to find segregating QTL for all the economic traits analyzed, and confidence intervals for QTL location ranged from ~5 to 35 million base pairs. Genomic estimated breeding values (GEBV) for milk production traits in the Israeli Holstein population were computed by the single-step method and compared to results for the two-step method. The single-step method was extended to derive GEBV for multi-parity evaluation. Long-term analysis of genomic selection demonstrated that inclusion of pedigree data from previous generations may result in less accurate GEBV. Major conclusions are: Predictions using single-step genomic best linear unbiased prediction (GBLUP) were the least biased, and that method appears to be the best tool for genomic evaluation of a small population, as it automatically accounts for parental index and allows for inclusion of female genomic information without additional steps. None of the methods applied to the Israeli Holstein population were able to derive GEBV for young bulls that were significantly better than parent averages. Thus we confirm previous studies that the main limiting factor for the accuracy of GEBV is the number of bulls with genotypes and progeny tests. Although 36 of the grandsires included in the APGD were genotyped for the BovineHDBeadChip, which includes 777,000 SNPs, we were not able to determine the causative polymorphism for any of the detected QTL. The number of valid unique markers on the BovineHDBeadChip is not sufficient for a reasonable probability to find the causative polymorphisms. Complete resequencing of the genome of approximately 50 bulls will be required, but this could not be accomplished within the framework of the current project due to funding constraints. Inclusion of pedigree data from older generations in the derivation of GEBV may result is less accurate evaluations.
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