Relevant bibliographies by topics / Errors correlated

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Errors correlated'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 February 2022

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Journal articles on the topic "Errors correlated"

1

Fischer, Paul E., and Robert E. Verrecchia. "Correlated Forecast Errors." Journal of Accounting Research 36, no. 1 (1998): 91. http://dx.doi.org/10.2307/2491322.

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Schweizer, Karl. "On Correlated Errors." European Journal of Psychological Assessment 28, no. 1 (September 2012): 1–2. http://dx.doi.org/10.1027/1015-5759/a000094.

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ARIMITSU, T., T. HAYASHI, S. KITAJIMA, and F. SHIBATA. "QUANTUM ERROR-CORRECTION FOR SPATIALLY CORRELATED ERRORS." International Journal of Quantum Information 06, supp01 (July 2008): 575–80. http://dx.doi.org/10.1142/s0219749908003803.

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It is shown that errors due to spatially correlated noises can be corrected by the quantum error-correction code and error-correction procedure prepared for those for independent noises. A model of noisy-channel which is under the influence of spatially correlated quantum Brownian motion is investigated within the framework of non-equilibrium thermo field dynamics that is a canonical operator formalism for dissipative quantum systems.
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Lu, Feng, and Dan C. Marinescu. "Quantum Error Correction of Time-correlated Errors." Quantum Information Processing 6, no. 4 (July 7, 2007): 273–93. http://dx.doi.org/10.1007/s11128-007-0058-1.

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Das, Rabindra Nath. "Slope Rotatability with Correlated Errors." Calcutta Statistical Association Bulletin 54, no. 1-2 (March 2003): 57–70. http://dx.doi.org/10.1177/0008068320030105.

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In Das (Cal. Statist. Assoc. Bull. 47, 1997. 199 -214) a study of second order rotatable designs with correlated errors was initiated. Robust second order rotatable designs under autocorrelated structures was developed. In this paper, general conditions for second order slope rotatability have been derived assuming errors have a general correlated error structure. Further, these conditions have been simplified under the intra-class structure of errors and verified with uncorrelated case.
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De Brabanter, K., J. De Brabanter, J. A. K. Suykens, and B. De Moor. "Kernel Regression with Correlated Errors." IFAC Proceedings Volumes 43, no. 6 (2010): 13–18. http://dx.doi.org/10.3182/20100707-3-be-2012.0001.

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Yang, Yuhong, Yuedong Wang, and Jean Opsomer. "Nonparametric Regressin with Correlated Errors." Statistical Science 16, no. 2 (May 2001): 134–53. http://dx.doi.org/10.1214/ss/1009213287.

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Kim, T. Y. "Nonparametric detection of correlated errors." Biometrika 91, no. 2 (June 1, 2004): 491–96. http://dx.doi.org/10.1093/biomet/91.2.491.

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RIFE, JASON, and DEMOZ GEBRE-EGZIABHER. "Symmetric Overbounding of Correlated Errors." Navigation 54, no. 2 (June 2007): 109–24. http://dx.doi.org/10.1002/j.2161-4296.2007.tb00398.x.

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Appadoo, S. S., A. Thavaneswaran, and Jagbir Singh. "RCA models with correlated errors." Applied Mathematics Letters 19, no. 8 (August 2006): 824–29. http://dx.doi.org/10.1016/j.aml.2005.11.003.

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Dissertations / Theses on the topic "Errors correlated"

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Stewart, Laura M. "Correlated observation errors in data assimilation." Thesis, University of Reading, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.553080.

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Data assimilation techniques combine observations and prior model forecasts to create initial conditions for numerical weather prediction (NWP). The relative weighting as- signed to each observation in the analysis is determined by the error associated with its measurement. Remote sensing data often have correlated errors, but the correlations are typically ignored in NWP. As operational centres move towards high-resolution fore- casting, the assumption of uncorrelated errors becomes impractical. This thesis provides new evidence that including observation error correlations in data assimilation schemes is both feasible and beneficial. We study the dual problem of quantifying and modelling observation error correlation structure. Firstly, in original work using statistics from the Met Office 4D- Var assimilation system, we diagnose strong cross-channel error eo- variances for the IASI satellite instrument. We then see how in a 3D- Var framework, information content is degraded under the assumption of uncorrelated errors, while re- tention of an approximate correlation gives clear benefits. These novel results motivate further study. We conclude by modelling observation error correlation structure in the framework of a one-dimensional shallow water model. Using an incremental 4D- Var assimilation system we observe that analysis errors are smallest when correlated error covariance matrix approximations are used over diagonal approximations. The new re- sults reinforce earlier conclusions on the benefits of including some error correlation structure.
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Pazman, Andrej, and Werner Müller. "Optimal Design of Experiments Subject to Correlated Errors." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2000. http://epub.wu.ac.at/64/1/document.pdf.

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In this paper we consider optimal design of experiments in the case of correlated observations, when no replications are possible. This situation is typical when observing a random process or random field with known covariance structure. We present a theorem which demonstrates that the computation of optimum exact designs corresponds to solving minimization problems in terms of design measures. (author's abstract)
Series: Forschungsberichte / Institut für Statistik
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Fedorov, Valery V., and Werner Müller. "Optimum design for correlated processes via eigenfunction expansions." Institut für Statistik und Mathematik, WU Vienna University of Economics and Business, 2004. http://epub.wu.ac.at/622/1/document.pdf.

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In this paper we consider optimum design of experiments for correlated observations. We approximate the error component of the process by an eigenvector expansion of the corresponding covariance function. Furthermore we study the limit behavior of an additional white noise as a regularization tool. The approach is illustrated by some typical examples. (authors' abstract)
Series: Research Report Series / Department of Statistics and Mathematics
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Adelaar, Glenn A. "An empirical investigation of nonlinear least squares estimation with correlated errors." Thesis, Georgia Institute of Technology, 1986. http://hdl.handle.net/1853/25586.

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Pazman, Andrej, and Werner Müller. "Extended Information Matrices for Optimal Designs when the Observations are Correlated." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 1996. http://epub.wu.ac.at/1416/1/document.pdf.

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Regression models with correlated errors lead to nonadditivity of the information matrix. This makes the usual approach of design optimization (approximation with a continuous design, application of an equivalence theorem, numerical calculations by a gradient algorithm) impossible. A method is presented that allows the construction of a gradient algorithm by altering the information matrices through adding of supplementary noise. A heuristic is formulated to circumvent the nonconvexity problem and the method is applied to typical examples from the literature. (author's abstract)
Series: Forschungsberichte / Institut für Statistik
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Stehlik, Milan. "Further aspects on an example of D-optimal designs in the case of correlated errors." Institut für Statistik und Mathematik, WU Vienna University of Economics and Business, 2004. http://epub.wu.ac.at/670/1/document.pdf.

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The aim of this paper is discussion on particular aspects of the extension of a classic example in the design of experiments under the presence of correlated errors. Such extension allows us to study the effect of the correlation range on the design. We discuss the dependence of the information gained by the D-optimum design on the covariance bandwidth and also we concentrate to some technical aspects that occurs in such settings. (author's abstract)
Series: Research Report Series / Department of Statistics and Mathematics
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Akdemir, Deniz. "Components Of Response Variance For Cluster Samples." Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/1206044/index.pdf.

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Measures of data quality are important for the evaluation and improvement of survey design and procedures. A detailed investigation of the sources, magnitude and impact of errors is necessary to identify how survey design and procedures may be improved and how resources allocated more efficiently among various aspects of the survey operation. A major part of this thesis is devoted to the overview of statistical theory and methods for measuring the contribution of response variability to the overall error of a survey. A very common practice in surveys is to select groups (clusters) of elements together instead of independent selection of elements. In practice cluster samples tend to produce higher sampling variance for statistics than element samples of the same size. Their frequent use stems from the desirable cost features that they have. Most data collection and sample designs involve some overlapping between interviewer workload and the sampling units (clusters). For those cases, a proportion of the measurement variance, which is due to interviewers, is reflected to some degree in the sampling variance calculations. The prime purpose in this thesis is to determine a variance formula that decomposes the total variance into sampling and measurement variance components for two commonly used data collection and sample designs. Once such a decomposition is obtained, determining an optimum allocation in existence of measurement errors would be possible.
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Savanhu, Richard. "Bayesian estimation of stochastic volatility models with fat tails and correlated errors applied to the South African financial market." Master's thesis, University of Cape Town, 2011. http://hdl.handle.net/11427/11085.

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Includes bibliographical references (leaves 39-40).
In this study we apply Markov Chain Monte Carlo methods in the Bayesian framework to estimate Stochastic Volatility models using South African financial market data. A single move Gibbs sampler is used to sample parameters from the posterior distribution. Volatility is used as measure of an asset's risk. It is particularly important in risk management, derivatives pricing, and portfolio selection. When pricing derivatives it is important to quote the correct volatility trading in the market, hence there is need for good estimates of volatility. To capture the stylised facts about asset returns we used the model extended for fat tails and correlated errors. To support this model against the basic model of Taylor (1986), we computed Bayes Factors of Jacquier, Polson and Ross (2004). The extended model was found to be far superior to the basic model.
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Lohnes, Mitchell T. "Multivariate approaches to qualitative and quantitative analysis in chemistry, I. Calibration with correlated errors; II. Window target-testing factor analysis." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ36495.pdf.

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Martins, Vera Lúcia Milani. "Comparação de combinação de previsões correlacionadas e não correlacionadas com as suas previsões individuais : um estudo com séries industriais." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2011. http://hdl.handle.net/10183/29051.

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A realização de previsões adequadas nas indústrias oportuniza o correto dimensionamento de diversos aspectos da gestão da produção. Um dos métodos empregados no intuito de melhorar a precisão das previsões é conhecido como combinação de previsões. Ao longo dos anos, foram publicados estudos de combinação a fim de comparar os métodos já existentes e indicar entre estes, qual o mais acurado. No entanto não há unanimidade em suas conclusões. Entre as combinações existentes, o método da média aritmética é reconhecido como um dos mais utilizados, enquanto que o método da variância mínima é por vezes apresentado como mais acurado e permite em sua formulação a consideração ou não da correlação entre os erros das previsões individuais. No intuito de identificar, para previsões em séries reais industriais, se existe diferença entre a acurácia das previsões individuais e de suas combinações é que este estudo está sendo proposto. A modelagem individual abordada é a ARIMA e a RNA e as medidas empregadas para a escolha do método mais preciso são MAPE, MAE e MSE. O trabalho está estruturado em três artigos, nos quais se realizam comparações entre técnicas de previsão individual e suas combinações. O Artigo 1 aborda a comparação entre as técnicas de previsão individual e as combinações por média aritmética e variância mínima simplificada. O Artigo 2, por sua vez, apresenta um estudo comparativo das técnicas de previsão individuais e as combinações por média aritmética e variância mínima, utilizando a correlação entre os erros na obtenção dos pesos de cada previsão. Por fim, um comparativo entre as três combinações que contemplam este estudo é explicitado no Artigo 3. Como principal resultado, destaca-se o desempenho superior obtido por meio dos métodos de combinação por variância mínima, em especial o método simplificado.
The adequate forecasting in industries allows the correct sizing of many aspects of production management. A method used to improve the precision of forecasts is the combination of predictions. Over time, many studies were conducted to evaluate the existent methods and to indicate which one is the most precise. However, there is no unanimity in those studies conclusions. Among the combination methods, the arithmetic average is recognized as the broadly applied, while the minimum variance is sometimes presented as more accurate allowing to consider the correlation between the errors of individual forecasts or not. This study proposes to identify, in real industrial predictions series, if there are differences between accuracy of individual forecasts and their combinations. The individual predictions are performed by ARIMA and ANN models, and the measures used to choose the best method are MAPE, MAE and MSE. This dissertation is structured as three articles, in which a series of comparisons between individual prediction techniques and their combinations. Article 1 addresses the comparison between the individual prevision techniques and the combination methods of mean arithmetic and simplified minimum variance. Article 2 presents a comparative study between the individual prevision techniques and the combination methods of mean arithmetic and minimum variance, considering errors correlated. The comparison between the three combinations presented in the previous articles is explained in Article 3. As main result of the dissertation, it is highlighted the superior performance obtained with the minimum variance combined methods, specially the simplified method.
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Books on the topic "Errors correlated"

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Boothe, Paul Michael. A differencing specification test for models with serially correlated errors. Kingston, Ont., Canada: Institute for Economic Research, Queen's University, 1985.

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Inoue, Atsushi. A portmanteau test for serially correlated errors in fixed effects models. Cambridge, MA: National Bureau of Economic Research, 2005.

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Knottnerus, Paul. Linear models with correlated disturbances. Berlin: Springer-Verlag, 1991.

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Brazil. O Plano de estabilização econômica: Decretos-leis 2,283 e 2,284 : acertos e erros, comentários de B. Calheiros Bomfim, legislação complementar e correlata. Rio de Janeiro-RJ: Edições Trabalhistas, 1986.

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J, Bordelon W., Coleman Hugh W, and United States. National Aeronautics and Space Administration., eds. Effect of correlated precision errors on uncertainty of a subsonic venturi calibration. [Washington, D.C: National Aeronautics and Space Administration, 1997.

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J, Bordelon W., Coleman Hugh W, and American Institute of Aeronautics and Astronautics., eds. Effect of correlated precision errors on uncertainty of a subsonic venturi calibration. Reston, VA: America Institute of Aeronautics and Astronautics, 1996.

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Armilio, Maria Luisa. Electrophysiological correlates of response inhibition and error processing: The effects of strategic manipulation, feedback and traumatic brain injury. 2002.

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Walsh, Bruce, and Michael Lynch. Selection Response in Natural Populations. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198830870.003.0020.

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The breeder's equation often fails when applied to natural populations. In large part, this likely occurs because the assumed trait is not the actual target of selection. A within-generation change in the mean of a suggested target trait can arise as a correlated response from selection acting elsewhere. This chapter examines sources of error in the breeder's equation and approaches that attempt to determine if an assumed trait is actually the true target of selection. It also reviews a number of long-term studies from natural populations and examines possible sources for the failure of most of these studies to conform to the expectations of the breeder's equation.
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Walsh, Bruce, and Michael Lynch. Analysis of Short-term Selection Experiments: 1. Least-squares Approaches. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198830870.003.0018.

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This chapter examines short-term (a few generations) selection response in the mean of a trait. Traditionally, such experiments are analyzed using least-squares (LS) approaches. While ordinary LS (OLS) is often used, genetic drift causes the residual to be both correlated and heteroscedastic, resulting in the sampling variances given by OLS being too small. This chapter details the appropriate general LS (GLS) approaches to properly account for this residual error structure. It also reviews some of the common features observed in short-term selection experiments and examines experimental designs, such as the use of a control population versus a divergence-selection approach. It concludes by discussing another linear model used mainly by plant breeders, generation-means analysis (GMA), wherein remnant seed for several generations of response are crossed and then grown in a common garden. Such an analysis can provide insight into the genetic nature of any response.
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Harnish, Stacy M. Anomia and Anomic Aphasia: Implications for Lexical Processing. Edited by Anastasia M. Raymer and Leslie J. Gonzalez Rothi. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199772391.013.7.

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Anomia is a term that describes the inability to retrieve a desired word, and is the most common deficit present across different aphasia syndromes. Anomic aphasia is a specific aphasia syndrome characterized by a primary deficit of word retrieval with relatively spared performance in other language domains, such as auditory comprehension and sentence production. Damage to a number of cognitive and motor systems can produce errors in word retrieval tasks, only subsets of which are language deficits. In the cognitive and neuropsychological underpinnings section, we discuss the major processing steps that occur in lexical retrieval and outline how deficits at each of the stages may produce anomia. The neuroanatomical correlates section will include a review of lesion and neuroimaging studies of language processing to examine anomia and anomia recovery in the acute and chronic stages. The assessment section will highlight how discrepancies in performance between tasks contrasting output modes and input modalities may provide insight into the locus of impairment in anomia. Finally, the treatment section will outline some of the rehabilitation techniques for forms of anomia, and take a closer look at the evidence base for different aspects of treatment.
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Book chapters on the topic "Errors correlated"

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Sen, Ashish, and Muni Srivastava. "*Correlated Errors." In Springer Texts in Statistics, 132–53. New York, NY: Springer New York, 1990. http://dx.doi.org/10.1007/978-1-4612-4470-7_7.

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Sen, Ashish, and Muni Srivastava. "Correlated Errors." In Springer Texts in Statistics, 132–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-662-25092-1_7.

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Sheather, Simon J. "Serially Correlated Errors." In Springer Texts in Statistics, 305–29. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-09608-7_9.

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Opsomer, J. D. "Nonparametric Regression in the Presence of Correlated Errors." In Modelling Longitudinal and Spatially Correlated Data, 339–48. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-0699-6_30.

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Hughes-Olivier, Jacqueline M. "Optimal Designs for Nonlinear Models With Correlated Errors." In Institute of Mathematical Statistics Lecture Notes - Monograph Series, 163–74. Hayward, CA: Institute of Mathematical Statistics, 1998. http://dx.doi.org/10.1214/lnms/1215456195.

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Gałecki, Andrzej, and Tomasz Burzykowski. "Linear Model with Fixed Effects and Correlated Errors." In Springer Texts in Statistics, 177–96. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3900-4_10.

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Gałecki, Andrzej, and Tomasz Burzykowski. "ARMD Trial: Modeling Correlated Errors for Visual Acuity." In Springer Texts in Statistics, 213–41. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3900-4_12.

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Nallagatla, Vishnu Priya, and Vinod Chandran. "Sequential Fusion Using Correlated Decisions for Controlled Verification Errors." In Computer Analysis of Images and Patterns, 49–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23678-5_4.

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Kulczycki, Piotr, and Malgorzata Charytanowicz. "Conditional Multidimensional Parameter Identification with Asymmetric Correlated Losses of Estimation Errors." In Neural Information Processing, 287–94. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12640-1_35.

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Gałecki, Andrzej, and Tomasz Burzykowski. "Fitting Linear Models with Fixed Effects and Correlated Errors: The gls() Function." In Springer Texts in Statistics, 197–212. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3900-4_11.

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Conference papers on the topic "Errors correlated"

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Yagi, Hideki, Toshiyasu Matsushima, and Shigeichi Hirasawa. "Error control codes for parallel channel with correlated errors." In 2008 IEEE Information Theory Workshop (ITW). IEEE, 2008. http://dx.doi.org/10.1109/itw.2008.4578699.

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BAGNASCO, CHIARA, YASUSHI KONDO, and MIKIO NAKAHARA. "A SIMPLE OPERATOR QUANTUM ERROR CORRECTION SCHEME AVOIDING FULLY CORRELATED ERRORS." In Summer Workshop on Physics, Mathematics, and All That Quantum Jazz. WORLD SCIENTIFIC, 2014. http://dx.doi.org/10.1142/9789814602372_0012.

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Deng, Mei, Y. C. Ho, and J. Q. Hu. "Effect of correlated estimation errors in ordinal optimization." In the 24th conference. New York, New York, USA: ACM Press, 1992. http://dx.doi.org/10.1145/167293.167403.

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Amal, Hmimou, Iaousse M'barek, El Hadri Zouhair, Hmimou Soumaia, Hachimi Hanaa, and El Kettani Yousfi Mohammed. "Treatment of Correlated Errors in Structural Equation Models." In 2021 7th International Conference on Optimization and Applications (ICOA). IEEE, 2021. http://dx.doi.org/10.1109/icoa51614.2021.9442624.

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Fan, Jihao, Min-Hsiu Hsieh, Hanwu Chen, He Chen, and Yonghui Li. "Construction and Performance of Quantum Burst Error Correction Codes for Correlated Errors." In 2018 IEEE International Symposium on Information Theory (ISIT). IEEE, 2018. http://dx.doi.org/10.1109/isit.2018.8437493.

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Chiesa, Simone, D. M. Ceperley, R. M. Martin, and Markus Holzmann. "Random phase approximation and the finite size errors in many body simulations." In LECTURES ON THE PHYSICS OF STRONGLY CORRELATED SYSTEMS XI: Eleventh Training Course in the Physics of Strongly Correlated Systems. AIP, 2007. http://dx.doi.org/10.1063/1.2751996.

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Thian, Boon Sim, and Andrea Goldsmith. "Decoding for MIMO systems with correlated channel estimation errors." In 2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2011. http://dx.doi.org/10.1109/allerton.2011.6120211.

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Thomas, E. V., C. L. Stork, and J. K. Mattingly. "Accounting for correlated errors in inverse radiation transport problems." In 2010 IEEE Nuclear Science Symposium and Medical Imaging Conference (2010 NSS/MIC). IEEE, 2010. http://dx.doi.org/10.1109/nssmic.2010.5873892.

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Pinel, C., P. A. Cross, S. H. Archer, and D. Ridyard. "The Effect of Correlated Navigation Errors on Marine Seismic Data." In 59th EAGE Conference & Exhibition. European Association of Geoscientists & Engineers, 1997. http://dx.doi.org/10.3997/2214-4609-pdb.131.gen1997_a040.

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Langel, Steven, Omar García Crespillo, and Mathieu Joerger. "Overbounding Sequential Estimation Errors Due to Non-Gaussian Correlated Noise." In 33rd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2020). Institute of Navigation, 2020. http://dx.doi.org/10.33012/2020.17576.

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Reports on the topic "Errors correlated"

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Canjels, Eugene, and Mark Watson. Estimating Deterministic Trends in the Presence of Serially Correlated Errors. Cambridge, MA: National Bureau of Economic Research, September 1994. http://dx.doi.org/10.3386/t0165.

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Inoue, Atsushi, and Gary Solon. A Portmanteau Test for Serially Correlated Errors in Fixed Effects Models. Cambridge, MA: National Bureau of Economic Research, June 2005. http://dx.doi.org/10.3386/t0310.

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Research Institute (IFPRI), International Food Policy. Correlated non-classical measurement errors, ‘second best’ policy inference and the inverse size-productivity relationship in agriculture. Washington, DC: International Food Policy Research Institute, 2018. http://dx.doi.org/10.2499/1024320684.

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Thomas, Edward V., Christopher L. Stork, and John K. Mattingly. Solving Inverse Radiation Transport Problems with Multi-Sensor Data in the Presence of Correlated Measurement and Modeling Errors. Office of Scientific and Technical Information (OSTI), July 2015. http://dx.doi.org/10.2172/1194405.

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