Relevant bibliographies by topics / Linear model

Contents

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

Academic literature on the topic 'Linear model'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 September 2023

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Journal articles on the topic "Linear model"

1

Kubáček, Lubomír. "Linear model with inaccurate variance components." Applications of Mathematics 41, no. 6 (1996): 433–45. http://dx.doi.org/10.21136/am.1996.134336.

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Wimmer, Gejza. "Linear-quadratic estimators in a special structure of the linear model." Applications of Mathematics 40, no. 2 (1995): 81–105. http://dx.doi.org/10.21136/am.1995.134282.

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Syafei, Arie Dipareza, Akimasa Fujiwara, and Junyi Zhang. "Prediction Model of Air Pollutant Levels Using Linear Model with Component Analysis." International Journal of Environmental Science and Development 6, no. 7 (2015): 519–25. http://dx.doi.org/10.7763/ijesd.2015.v6.648.

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STUTE, WINFRIED, and LI-XING ZHU. "Model Checks for Generalized Linear Models." Scandinavian Journal of Statistics 29, no. 3 (September 2002): 535–45. http://dx.doi.org/10.1111/1467-9469.00304.

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Borowiak, Dale. "Linear Models: A Mean Model Approach." Technometrics 39, no. 4 (November 1997): 425–26. http://dx.doi.org/10.1080/00401706.1997.10485163.

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Anderson-Cook, C. M. "Linear models: A mean model approach." Journal of Statistical Planning and Inference 64, no. 1 (October 1997): 153–55. http://dx.doi.org/10.1016/s0378-3758(97)86520-2.

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Champ, Charles W. "Linear Models: A Mean Model Approach." Journal of Quality Technology 32, no. 2 (April 2000): 191–92. http://dx.doi.org/10.1080/00224065.2000.11979992.

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Müller, Samuel, J. L. Scealy, and A. H. Welsh. "Model Selection in Linear Mixed Models." Statistical Science 28, no. 2 (May 2013): 135–67. http://dx.doi.org/10.1214/12-sts410.

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Muske, Kenneth R., and James B. Rawlings. "Model predictive control with linear models." AIChE Journal 39, no. 2 (February 1993): 262–87. http://dx.doi.org/10.1002/aic.690390208.

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Shalabh. "Linear Model Methodology." Journal of the Royal Statistical Society: Series A (Statistics in Society) 173, no. 4 (September 20, 2010): 935–36. http://dx.doi.org/10.1111/j.1467-985x.2010.00663_5.x.

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Dissertations / Theses on the topic "Linear model"

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Brien, Christopher J. "Factorial linear model analysis." Title page, table of contents and summary only, 1992. http://thesis.library.adelaide.edu.au/public/adt-SUA20010530.175833.

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"February 1992" Bibliography: leaf 323-344. Electronic publication; Full text available in PDF format; abstract in HTML format. Develops a general strategy for factorial linear model analysis for experimental and observational studies, an iterative, four-stage, model comparison procedure. The approach is applicable to studies characterized as being structure-balanced, multitiered and based on Tjur structures unless the structure involves variation factors when it must be a regular Tjur structure. It covers a wide range of experiments including multiple-error, change-over, two-phase, superimposed and unbalanced experiments. Electronic reproduction.[Australia] :Australian Digital Theses Program,2001.
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Greenaway, Mark Jonathan. "Numerically Stable Approximate Bayesian Methods for Generalized Linear Mixed Models and Linear Model Selection." Thesis, The University of Sydney, 2019. http://hdl.handle.net/2123/20233.

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Approximate Bayesian inference methods offer methodology for fitting Bayesian models as fast alternatives to Markov Chain Monte Carlo methods that sometimes have only a slight loss of accuracy. In this thesis, we consider variable selection for linear models, and zero inflated mixed models. Variable selection for linear regression models are ubiquitous in applied statistics. We use the popular g-prior (Zellner, 1986) for model selection of linear models with normal priors where g is a prior hyperparameter. We derive exact expressions for the model selection Bayes Factors in terms of special functions depending on the sample size, number of covariates and R-squared of the model. We show that these expressions are accurate, fast to evaluate, and numerically stable. An R package blma for doing Bayesian linear model averaging using these exact expressions has been released on GitHub. We extend the Particle EM method of (Rockova, 2017) using Particle Variational Approximation and the exact posterior marginal likelihood expressions to derive a computationally efficient algorithm for model selection on data sets with many covariates. Our algorithm performs well relative to existing algorithms, completing in 8 seconds on a model selection problem with a sample size of 600 and 7200 covariates. We consider zero-inflated models that have many applications in areas such as manufacturing and public health, but pose numerical issues when fitting them to data. We apply a variational approximation to zero-inflated Poisson mixed models with Gaussian distributed random effects using a combination of VB and the Gaussian Variational Approximation (GVA). We also incorporate a novel parameterisation of the covariance of the GVA using the Cholesky factor of the precision matrix, similar to Tan and Nott (2018) to resolve associated numerical difficulties.
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Townsend, Shane Martin Joseph. "Non-linear model predictive control." Thesis, Queen's University Belfast, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.301061.

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Waterman, Megan Janet Tuttle. "Linear Mixed Model Robust Regression." Diss., Virginia Tech, 2002. http://hdl.handle.net/10919/27708.

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Mixed models are powerful tools for the analysis of clustered data and many extensions of the classical linear mixed model with normally distributed response have been established. As with all parametric models, correctness of the assumed model is critical for the validity of the ensuing inference. Model robust regression techniques predict mean response as a convex combination of a parametric and a nonparametric model fit to the data. It is a semiparametric method by which incompletely or incorrectly specified parametric models can be improved through adding an appropriate amount of a nonparametric fit. We apply this idea of model robustness in the framework of the linear mixed model. The mixed model robust regression (MMRR) predictions we propose are convex combinations of predictions obtained from a standard normal-theory linear mixed model, which serves as the parametric model component, and a locally weighted maximum likelihood fit which serves as the nonparametric component. An application of this technique with real data is provided.
Ph. D.
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Zurcher, James. "Model-based knowledge acquisition using adaptive piecewise linear models." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0018/NQ46956.pdf.

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Asterios, Geroukis. "Prediction of Linear Models: Application of Jackknife Model Averaging." Thesis, Uppsala universitet, Statistiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-297671.

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When using linear models, a common practice is to find the single best model fit used in predictions. This on the other hand can cause potential problems such as misspecification and sometimes even wrong models due to spurious regression. Another method of predicting models introduced in this study as Jackknife Model Averaging developed by Hansen & Racine (2012). This assigns weights to all possible models one could use and allows the data to have heteroscedastic errors. This model averaging estimator is compared to the Mallows’s Model Averaging (Hansen, 2007) and model selection by Bayesian Information Criterion and Mallows’s Cp. The results show that the Jackknife Model Averaging technique gives less prediction errors compared to the other methods of model prediction. This study concludes that the Jackknife Model Averaging technique might be a useful choice when predicting data.
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Overstall, Antony Marshall. "Default Bayesian model determination for generalised linear mixed models." Thesis, University of Southampton, 2010. https://eprints.soton.ac.uk/170229/.

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In this thesis, an automatic, default, fully Bayesian model determination strategy for GLMMs is considered. This strategy must address the two key issues of default prior specification and computation. Default prior distributions for the model parameters, that are based on a unit information concept, are proposed. A two-phase computational strategy, that uses a reversible jump algorithm and implementation of bridge sampling, is also proposed. This strategy is applied to four examples throughout this thesis.
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Sima, Adam. "Accounting for Model Uncertainty in Linear Mixed-Effects Models." VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/2950.

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Standard statistical decision-making tools, such as inference, confidence intervals and forecasting, are contingent on the assumption that the statistical model used in the analysis is the true model. In linear mixed-effect models, ignoring model uncertainty results in an underestimation of the residual variance, contributing to hypothesis tests that demonstrate larger than nominal Type-I errors and confidence intervals with smaller than nominal coverage probabilities. A novel utilization of the generalized degrees of freedom developed by Zhang et al. (2012) is used to adjust the estimate of the residual variance for model uncertainty. Additionally, the general global linear approximation is extended to linear mixed-effect models to adjust the standard errors of the parameter estimates for model uncertainty. Both of these methods use a perturbation method for estimation, where random noise is added to the response variable and, conditional on the observed responses, the corresponding estimate is calculated. A simulation study demonstrates that when the proposed methodologies are utilized, both the variance and standard errors are inflated for model uncertainty. However, when a data-driven strategy is employed, the proposed methodologies show limited usefulness. These methods are evaluated with a trial assessing the performance of cervical traction in the treatment of cervical radiculopathy.
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Vazirinejad, Shamsedin. "Model identification and parameter estimation of stochastic linear models." Diss., The University of Arizona, 1990. http://hdl.handle.net/10150/185037.

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It is well known that when the input variables of the linear regression model are subject to noise contamination, the model parameters can not be estimated uniquely. This, in the statistical literature, is referred to as the identifiability problem of the errors-in-variables models. Further, in linear regression there is an explicit assumption of the existence of a single linear relationship. The statistical properties of the errors-in-variables models under the assumption that the noise variances are either known or that they can be estimated are well documented. In many situations, however, such information is neither available nor obtainable. Although under such circumstances one can not obtain a unique vector of parameters, the space, Ω, of the feasible solutions can be computed. Additionally, assumption of existence of a single linear relationship may be presumptuous as well. A multi-equation model similar to the simultaneous-equations models of econometrics may be more appropriate. The goals of this dissertation are the following: (1) To present analytical techniques or algorithms to reduce the solution space, Ω, when any type of prior information, exact or relative, is available; (2) The data covariance matrix, Σ, can be examined to determine whether or not Ω is bounded. If Ω is not bounded a multi-equation model is more appropriate. The methodology for identifying the subsets of variables within which linear relations can feasibly exist is presented; (3) Ridge regression technique is commonly employed in order to reduce the ills caused by collinearity. This is achieved by perturbing the diagonal elements of Σ. In certain situations, applying ridge regression causes some of the coefficients to change signs. An analytical technique is presented to measure the amount of perturbation required to render such variables ineffective. This information can assist the analyst in variable selection as well as deciding on the appropriate model; (4) For the situations when Ω is bounded, a new weighted regression technique based on the computed upper bounds on the noise variances is presented. This technique will result in identification of a unique estimate of the model parameters.
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Sammut, Fiona. "Using generalized linear models to model compositional response data." Thesis, University of Warwick, 2016. http://wrap.warwick.ac.uk/89876/.

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This work proposes a multivariate logit model which models the influence of explanatory variables on continuous compositional response variables. This multivariate logit model generalizes an elegant method that was suggested previously by Wedderburn (1974) for the analysis of leaf blotch data in the special case of J = 2, leading to our naming this new approach as the generalized Wedderburn method. In contrast to the logratio modelling approach devised by Aitchison (1982, J. Roy Stat. Soc. B.), the multivariate logit model used under the generalized Wedderburn approach models the expectation of a compositional response variable directly and is also able to handle zeros in the data. The estimation of the parameters in the new model is carried out using the technique of generalized estimating equations (GEE). This technique relies on the specification of a working variance-covariance structure. A working variance-covariance structure which caters for the specific variability arising in compositional data is derived. The GEE estimator that is used to estimate the parameters of the multivariate logit model is shown to be invariant to the values of the correlation and dispersion parameters in the working variance-covariance structure. Due to this invariance property and the fact that the estimating equations used under the generalized Wedderburn method are linear and unbiased, the GEE estimator achieves full efficiency across a wide class of potential dispersion and correlation matrices for the compositional response variables. As for any other GEE estimator, the estimator used in the generalized Wedderburn method is also asymptotically unbiased and consistent, provided that the marginal mean model specification is correct. The theoretical results derived in this thesis are substantiated by simulation experiments, and properties of the new model are also studied empirically on some classic datasets from the literature.
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Books on the topic "Linear model"

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Khuri, André I. Linear model methodology. Boca Raton: Chapman & Hall/CRC, 2010.

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Muller, Keith E., and Paul W. Stewart. Linear Model Theory. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2006. http://dx.doi.org/10.1002/0470052147.

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Zimmerman, Dale L. Linear Model Theory. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52063-2.

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Zimmerman, Dale L. Linear Model Theory. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52074-8.

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Khuri, André I. Linear model methodology. Boca Raton: Chapman & Hall/CRC, 2010.

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Khuri, André I. Linear model methodology. Boca Raton: CRC Press, 2010.

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Khuri, André I. Linear model methodology. Boca Raton: Chapman & Hall/CRC, 2010.

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Moser, Barry Kurt. Linear models: A mean model approach. San Diego: Academic Press, 1996.

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Geological Survey (U.S.), ed. Linear Q model calculations. Menlo Park, Ca: U.S. Dept. of the Interior, Geological Survey, 1985.

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Seber, George. The Linear Model and Hypothesis. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21930-1.

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Book chapters on the topic "Linear model"

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Wang, Rui-Sheng. "Linear Model." In Encyclopedia of Systems Biology, 1133. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_382.

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Jurečková, Jana, Jan Picek, and Martin Schindler. "Linear model." In Robust Statistical Methods with R, 93–144. Second edition. | Boca Raton, Florida : CRC Press, [2019]: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/b21993-5.

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Mehtätalo, Lauri, and Juha Lappi. "Linear Model." In Biometry for Forestry and Environmental Data, 67–130. Boca Raton, FL : CRC Press, 2020. | Series: Chapman & Hall/CRC applied environmental statistics: Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9780429173462-4.

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Zimmerman, Dale L. "Model Misspecification." In Linear Model Theory, 279–300. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52063-2_12.

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Zimmerman, Dale L. "Model Misspecification." In Linear Model Theory, 171–84. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52074-8_12.

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de Alencar, Marcelo Sampaio, Raphael Tavares de Alencar, Raissa Bezerra Rocha, and Ana Isabela Cunha. "Operational Amplifier Model." In Linear Electronics, 107–20. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003338758-8.

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McCullagh, P., and J. A. Nelder. "Model checking." In Generalized Linear Models, 391–418. Boston, MA: Springer US, 1989. http://dx.doi.org/10.1007/978-1-4899-3242-6_12.

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Hoffmann, John P. "Model Specification." In Linear Regression Models, 241–74. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003162230-12.

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Zimmerman, Dale L. "A Brief Introduction." In Linear Model Theory, 1–5. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52063-2_1.

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Zimmerman, Dale L. "Constrained Least Squares Estimation and ANOVA." In Linear Model Theory, 201–37. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52063-2_10.

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Conference papers on the topic "Linear model"

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Wagner, Albrecht. "The physics potential of an e+e− linear collider." In Beyond the standard model. American Institute of Physics, 1997. http://dx.doi.org/10.1063/1.54475.

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Qin, Zhengda, Badong Chen, Nanning Zheng, and Jose C. Principe. "Augmented Space Linear Model." In 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 2018. http://dx.doi.org/10.1109/ijcnn.2018.8489457.

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Wenyi Zeng and Xin Zheng. "Fuzzy Linear Regression Model." In 2008 International Symposium on Information Science and Engineering (ISISE). IEEE, 2008. http://dx.doi.org/10.1109/isise.2008.143.

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Jikia, Georgy. "Pair production of W bosons at the photon linear collider: a window to the electroweak symmetry breaking?" In Beyond the standard model. American Institute of Physics, 1997. http://dx.doi.org/10.1063/1.54504.

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Singh, Deepesh K., and Sjoerd W. Rienstra. "A systematic impedance model for non-linear Helmholtz resonator liner." In 19th AIAA/CEAS Aeroacoustics Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2013. http://dx.doi.org/10.2514/6.2013-2223.

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Li, Yan, Jinrong Cui, Jun Ye, Zixian Zhu, and Guangpeng Xiao. "Efficient Energy Linear Programming Model." In 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS). IEEE, 2018. http://dx.doi.org/10.1109/ccis.2018.8691164.

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Chauca, J., R. Doria, and W. Soares. "Non-linear Abelian gauge model." In THE SIXTH INTERNATIONAL SCHOOL ON FIELD THEORY AND GRAVITATION-2012. AIP, 2012. http://dx.doi.org/10.1063/1.4756979.

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"LINEAR MODEL FOR CANAL POOLS." In 8th International Conference on Informatics in Control, Automation and Robotics. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003536103060313.

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Scarponcini, Paul. "Generalized model for linear referencing." In the seventh ACM international symposium. New York, New York, USA: ACM Press, 1999. http://dx.doi.org/10.1145/320134.320149.

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Gheno, Gloria. "MEDIATION IN LOG-LINEAR MODEL." In 23rd International Academic Conference, Venice. International Institute of Social and Economic Sciences, 2016. http://dx.doi.org/10.20472/iac.2016.023.103.

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Reports on the topic "Linear model"

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Lester, Brian T., and Kevin Nicholas Long. Modular Linear Thermoviscoelastic Model. Office of Scientific and Technical Information (OSTI), May 2020. http://dx.doi.org/10.2172/1619910.

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Momozaki, Yoichi. A model of annular linear induction pumps. Office of Scientific and Technical Information (OSTI), October 2016. http://dx.doi.org/10.2172/1331318.

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Jones, Michael J., and Tomaso Poggio. Model-Based Matching of Line Drawings by Linear Combinations of Prototypes. Fort Belvoir, VA: Defense Technical Information Center, December 1995. http://dx.doi.org/10.21236/ada307099.

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Hellerman, Simeon. Linear Sigma Model Toolshed for D-brane Physics. Office of Scientific and Technical Information (OSTI), August 2001. http://dx.doi.org/10.2172/787182.

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Long, Kevin Nicholas, and Judith Alice Brown. A Linear Viscoelastic Model Calibration of Sylgard 184. Office of Scientific and Technical Information (OSTI), April 2017. http://dx.doi.org/10.2172/1365535.

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Hartley, D. S. III. Confirming the Lanchestrian linear-logarithmic model of attrition. Office of Scientific and Technical Information (OSTI), December 1990. http://dx.doi.org/10.2172/6077918.

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Stevens, Mark R., and J. R. Beveridge. Optical Linear Feature Detection Based on Model Pose. Fort Belvoir, VA: Defense Technical Information Center, December 1995. http://dx.doi.org/10.21236/ada308546.

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Kundu, Debasis, and Amit Mitra. Estimating the Parameters of the Linear Compartment Model. Fort Belvoir, VA: Defense Technical Information Center, May 1998. http://dx.doi.org/10.21236/ada358190.

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Howett, Gerald L. Linear opponent-colors model optimized for brightness prediction. Gaithersburg, MD: National Bureau of Standards, 1986. http://dx.doi.org/10.6028/nbs.ir.85-3202.

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Ghosh, Subir. Influential Nonegligible Parameters under the Search Linear Model. Fort Belvoir, VA: Defense Technical Information Center, April 1986. http://dx.doi.org/10.21236/ada170079.

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