Academic literature on the topic 'Best linear unbiased estimator'

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Journal articles on the topic "Best linear unbiased estimator"

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Štulajter, František. "Robustness of the best linear unbiased estimator and predictor in linear regression models." Applications of Mathematics 35, no. 2 (1990): 162–68. http://dx.doi.org/10.21136/am.1990.104398.

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Sjöberg, Lars. "On the Best Quadratic Minimum Bias Non-Negative Estimator of a Two-Variance Component Model." Journal of Geodetic Science 1, no. 3 (September 1, 2011): 280–85. http://dx.doi.org/10.2478/v10156-011-0006-y.

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On the Best Quadratic Minimum Bias Non-Negative Estimator of a Two-Variance Component ModelVariance components (VCs) in linear adjustment models are usually successfully computed by unbiased estimators. However, for many unbiased VC techniques estimated variance components might be negative, a result that cannot be tolerated by the user. This is, for example, the case with the simple additive VC model aσ2/1 + bσ2/2 with known coefficients a and b, where either of the unbiasedly estimated variance components σ2/1 + σ2/2 may frequently come out negative. This fact calls for so-called non-negative VC estimators. Here the Best Quadratic Minimum Bias Non-negative Estimator (BQMBNE) of a two-variance component model is derived. A special case with independent observations is explicitly presented.
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Liu, Yonghui. "On equality of ordinary least squares estimator, best linear unbiased estimator and best linear unbiased predictor in the general linear model." Journal of Statistical Planning and Inference 139, no. 4 (April 2009): 1522–29. http://dx.doi.org/10.1016/j.jspi.2008.08.015.

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Wang, Xiang. "A best linear unbiased estimator for multi-seam deposits." International Journal of Mining and Geological Engineering 6, no. 3 (October 1988): 259–66. http://dx.doi.org/10.1007/bf00880977.

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Wu, Jibo, and Chaolin Liu. "The best linear unbiased estimator in a singular linear regression model." Statistical Papers 59, no. 3 (July 28, 2016): 1193–204. http://dx.doi.org/10.1007/s00362-016-0811-6.

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Puntanen, Simo, George P. H. Styan, and Hans Joachim Werner. "Two matrix-based proofs that the linear estimator Gy is the best linear unbiased estimator." Journal of Statistical Planning and Inference 88, no. 2 (August 2000): 173–79. http://dx.doi.org/10.1016/s0378-3758(00)00076-8.

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Mäkinen, J. "A bound for the Euclidean norm of the difference between the best linear unbiased estimator and a linear unbiased estimator." Journal of Geodesy 76, no. 6-7 (July 1, 2002): 317–22. http://dx.doi.org/10.1007/s00190-002-0262-9.

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Baksalary, Oskar Maria, and Götz Trenkler. "A projector oriented approach to the best linear unbiased estimator." Statistical Papers 50, no. 4 (August 2009): 721–33. http://dx.doi.org/10.1007/s00362-009-0252-6.

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Wu, Jong-Wuu, Sheau-Chiann Chen, Wen-Chuan Lee, and Heng-Yi Lai. "Weighted Moments Estimators of the Parameters for the Extreme Value Distribution Based on the Multiply Type II Censored Sample." Scientific World Journal 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/281624.

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We propose the weighted moments estimators (WMEs) of the location and scale parameters for the extreme value distribution based on the multiply type II censored sample. Simulated mean squared errors (MSEs) of best linear unbiased estimator (BLUE) and exact MSEs of WMEs are compared to study the behavior of different estimation methods. The results show the best estimator among the WMEs and BLUE under different combinations of censoring schemes.
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Schaden, Daniel, and Elisabeth Ullmann. "On Multilevel Best Linear Unbiased Estimators." SIAM/ASA Journal on Uncertainty Quantification 8, no. 2 (January 2020): 601–35. http://dx.doi.org/10.1137/19m1263534.

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Dissertations / Theses on the topic "Best linear unbiased estimator"

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Zhang, Keshu. "Best linear unbiased estimation fusion with constraints." ScholarWorks@UNO, 2003. http://louisdl.louislibraries.org/u?/NOD,86.

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Thesis (Ph. D.)--University of New Orleans, 2003.
Title from electronic submission form. "A dissertation ... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Electrical Engineering"--Dissertation t.p. Vita. Includes bibliographical references.
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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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Haq, Abdul. "Improvements in ranked set sampling." Thesis, University of Canterbury. Mathematics and Statistics, 2014. http://hdl.handle.net/10092/9661.

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The main focus of many agricultural, ecological and environmental studies is to develop well designed, cost-effective and efficient sampling designs. Ranked set sampling (RSS) is one of those sampling methods that can help accomplish such objectives by incorporating prior information and expert knowledge to the design. In this thesis, new RSS schemes are suggested for efficiently estimating the population mean. These sampling schemes can be used as cost-effective alternatives to the traditional simple random sampling (SRS) and RSS schemes. It is shown that the mean estimators under the proposed sampling schemes are at least as efficient as the mean estimator with SRS. We consider the best linear unbiased estimators (BLUEs) and the best linear invariant estimators (BLIEs) for the unknown parameters (location and scale) of a location-scale family of distributions under double RSS (DRSS) scheme. The BLUEs and BLIEs with DRSS are more precise than their counterparts based on SRS and RSS schemes. We also consider the BLUEs based on DRSS and ordered DRSS (ODRSS) schemes for the unknown parameters of a simple linear regression model using replicated observations. It turns out that, in terms of relative efficiencies, the BLUEs under ODRSS are better than the BLUEs with SRS, RSS, ordered RSS (ORSS) and DRSS schemes. Quality control charts are widely recognized for their potential to be a powerful process monitoring tool of the statistical process control. These control charts are frequently used in many industrial and service organizations to monitor in-control and out-of-control performances of a production or manufacturing process. The RSS schemes have had considerable attention in the construction of quality control charts. We propose new exponentially weighted moving average (EWMA) control charts for monitoring the process mean and the process dispersion based on the BLUEs obtained under ORSS and ODRSS schemes. We also suggest an improved maximum EWMA control chart for simultaneously monitoring the process mean and dispersion based on the BLUEs with ORSS scheme. The proposed EWMA control charts perform substantially better than their counterparts based on SRS and RSS schemes. Finally, some new EWMA charts are also suggested for monitoring the process dispersion using the best linear unbiased absolute estimators of the scale parameter under SRS and RSS schemes.
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Teixeira, Marcos Vinícius. "Estudos sobre a implementação online de uma técnica de estimação de energia no calorímetro hadrônico do atlas em cenários de alta luminosidade." Universidade Federal de Juiz de Fora (UFJF), 2015. https://repositorio.ufjf.br/jspui/handle/ufjf/4169.

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CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Este trabalho tem como objetivo o estudo de técnicas para a estimação da amplitude de sinais no calorímetro de telhas (TileCal) do ATLAS no LHC em cenários de alta luminosidade. Em alta luminosidade, sinais provenientes de colisões adjacentes são observados, ocasionando o efeito de empilhamento de sinais. Neste ambiente, o método COF (do inglês, Constrained Optimal Filter), apresenta desempenho superior ao algoritmo atualmente implementado no sistema. Entretanto, o COF requer a inversão de matrizes para o cálculo da pseudo-inversa de uma matriz de convolução, dificultando sua implementação online. Para evitar a inversão de matrizes, este trabalho apresenta métodos interativos, para a daptação do COF, que resultam em operações matemáticas simples. Baseados no Gradiente Descendente, os resultados demonstraram que os algoritmos são capazes de estimar a amplitude de sinais empilhados, além do sinal de interesse com eficiência similar ao COF. Visando a implementação online, este trabalho apresenta estudos sobre a complexidade dos métodos iterativos e propõe uma arquitetura de processamento em FPGA. Baseado em uma estrutura sequencial e utilizando lógica aritmética em ponto fixo, os resultados demonstraram que a arquitetura desenvolvida é capaz executar o método iterativo, atendendo os requisitos de tempo de processamento exigidos no TileCal.
This work aims at the study of techniques for online energy estimation in the ATLAS hadronic Calorimeter (TileCal) on the LHC collider. During further periods of the LHC operation, signals coming from adjacent collisions will be observed within the same window, producing a signal superposition. In this environment, the energy reconstruction method COF (Constrained Optimal Filter) outperforms the algorithm currently implemented in the system. However , the COF method requires an inversion of matrices and its online implementation is not feasible. To avoid such inversion of matrices, this work presents iteractive methods to implement the COF, resulting in simple mathematical operations. Based on the Gradient Descent, the results demonstrate that the algorithms are capable of estimating the amplitude of the superimposed signals with efficiency similar to COF. In addition, a processing architecture for FPGA implementation is proposed. The analysis has shown that the algorithms can be implemented in the new TilaCal electronics, reaching the processing time requirements.
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Krishnan, Rajet. "Problems in distributed signal processing in wireless sensor networks." Thesis, Manhattan, Kan. : Kansas State University, 2009. http://hdl.handle.net/2097/1351.

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Zhao, Zhanlue. "Performance Appraisal of Estimation Algorithms and Application of Estimation Algorithms to Target Tracking." ScholarWorks@UNO, 2006. http://scholarworks.uno.edu/td/394.

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This dissertation consists of two parts. The first part deals with the performance appraisal of estimation algorithms. The second part focuses on the application of estimation algorithms to target tracking. Performance appraisal is crucial for understanding, developing and comparing various estimation algorithms. In particular, with the evolvement of estimation theory and the increase of problem complexity, performance appraisal is getting more and more challenging for engineers to make comprehensive conclusions. However, the existing theoretical results are inadequate for practical reference. The first part of this dissertation is dedicated to performance measures which include local performance measures, global performance measures and model distortion measure. The second part focuses on application of the recursive best linear unbiased estimation (BLUE) or lineae minimum mean square error (LMMSE) estimation to nonlinear measurement problem in target tracking. Kalman filter has been the dominant basis for dynamic state filtering for several decades. Beyond Kalman filter, a more fundamental basis for the recursive best linear unbiased filtering has been thoroughly investigated in a series of papers by Dr. X. Rong Li. Based on the so-called quasirecursive best linear unbiased filtering technique, the constraints of the Kalman filter Linear-Gaussian assumptions can be relaxed such that a general linear filtering technique for nonlinear systems can be achieved. An approximate optimal BLUE filter is implemented for nonlinear measurements in target tracking which outperforms the existing method significantly in terms of accuracy, credibility and robustness.
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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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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
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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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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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Books on the topic "Best linear unbiased estimator"

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Geological Survey (U.S.), ed. KRIGING: An interactive program to determine the best linear unbiased estimation. [Reston, Va.?]: U.S. Dept. of the Interior, Geological Survey, 1985.

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Book chapters on the topic "Best linear unbiased estimator"

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Puntanen, Simo, and George P. H. Styan. "Best Linear Unbiased Estimation in Linear Models." In International Encyclopedia of Statistical Science, 141–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_143.

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Zimmerman, Dale L. "Best Linear Unbiased Estimation for the Aitken Model." In Linear Model Theory, 239–77. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52063-2_11.

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Zimmerman, Dale L. "Best Linear Unbiased Estimation for the Aitken Model." In Linear Model Theory, 153–69. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52074-8_11.

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Ferreira, Sandra S., Dário Ferreira, Célia Nunes, Francisco Carvalho, and João Tiago Mexia. "Orthogonal Block Structure and Uniformly Best Linear Unbiased Estimators." In Contributions to Statistics, 89–98. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17519-1_7.

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Balakrishnan, N., and William W. S. Chen. "Best Linear Unbiased Estimation of Location and Scale Parameters." In Handbook of Tables for Order Statistics from Lognormal Distributions with Applications, 13–15. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5309-0_4.

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Balakrishnan, N., and C. R. Rao. "Large-Sample Approximations to the Best Linear Unbiased Estimation and Best Linear Unbiased Prediction Based on Progressively Censored Samples and Some Applications." In Advances in Statistical Decision Theory and Applications, 431–44. Boston, MA: Birkhäuser Boston, 1997. http://dx.doi.org/10.1007/978-1-4612-2308-5_28.

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Clark, Samuel A., and Julius van der Werf. "Genomic Best Linear Unbiased Prediction (gBLUP) for the Estimation of Genomic Breeding Values." In Methods in Molecular Biology, 321–30. Totowa, NJ: Humana Press, 2013. http://dx.doi.org/10.1007/978-1-62703-447-0_13.

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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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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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Conference papers on the topic "Best linear unbiased estimator"

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Comuniello, Antonella, Alessio De Angelis, and Antonio Moschitta. "Ultrasound TDoA positioning using the Best Linear Unbiased Estimator." In 2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). IEEE, 2019. http://dx.doi.org/10.1109/i2mtc.2019.8827071.

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Lang, Oliver, Alexander Onic, Markus Steindl, and Mario Huemer. "Constrained Best Linear and Widely Linear Unbiased Estimation." In 2018 52nd Asilomar Conference on Signals, Systems, and Computers. IEEE, 2018. http://dx.doi.org/10.1109/acssc.2018.8645155.

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Liang, Ao, Fu Yun, and Zeng Zhaoyang. "Best linear unbiased estimation method for ammunition storage reliability data." In EM). IEEE, 2009. http://dx.doi.org/10.1109/icieem.2009.5344632.

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Zhang, K., and X. R. Li. "Optimal sensor data quantization for best linear unbiased estimation fusion." In 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601). IEEE, 2004. http://dx.doi.org/10.1109/cdc.2004.1428861.

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Dikici, Engin, Fredrik Orderud, and Hans Torp. "Best linear unbiased estimator for Kalman filter based left ventricle tracking in 3D+T echocardiography." In 2012 IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA). IEEE, 2012. http://dx.doi.org/10.1109/mmbia.2012.6164741.

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Wu, Jwo-Yuh, and Ling-Hua Chang. "Channel-aware distributed best-linear-unbiased estimation with reduced communication overheads." In ICC 2012 - 2012 IEEE International Conference on Communications. IEEE, 2012. http://dx.doi.org/10.1109/icc.2012.6363845.

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Niu, Dunbiao, Linxia Zhang, Enbin Song, and Yunmin Zhu. "The Equivalence Between Distributed and Centralized Best Linear Unbiased Estimation Fusion." In 2018 21st International Conference on Information Fusion (FUSION 2018). IEEE, 2018. http://dx.doi.org/10.23919/icif.2018.8455320.

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Wang, Pengfei, Hang Zhang, Liu Yang, and Yue Xiao. "An Improved Best Linear Unbiased Estimate with Conjugate Gradient for Channel Estimation in Massive MIMO Systems." In 2019 IEEE 3rd Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC). IEEE, 2019. http://dx.doi.org/10.1109/imcec46724.2019.8984045.

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Wu, Jwo-Yuh, and Tsang-Yi Wang. "Power allocation for robust distributed Best-Linear-Unbiased Estimation against sensing noise variance uncertainty." In 2011 IEEE 12th Workshop on Signal Processing Advances in Wireless Communications (SPAWC 2011). IEEE, 2011. http://dx.doi.org/10.1109/spawc.2011.5990391.

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Pladdy, C., S. Ozen, M. J. Fimoff, S. M. Nerayanuru, and M. D. Zoltowsko. "Best linear unbiased channel estimation for frequency selective multipath channels with long delay spreads." In 2003 IEEE 58th Vehicular Technology Conference. VTC 2003-Fall (IEEE Cat. No.03CH37484). IEEE, 2003. http://dx.doi.org/10.1109/vetecf.2003.1285220.

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