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Relevant bibliographies by topics / Mixed model

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Academic literature on the topic 'Mixed model'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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Journal articles on the topic "Mixed model"

1

Pødenphant, Sofie, Minh H. Truong, Kasper Kristensen, and Per B. Brockhoff. "The Mixed Assessor Model and the multiplicative mixed model." Food Quality and Preference 74 (June 2019): 38–48. http://dx.doi.org/10.1016/j.foodqual.2018.11.006.

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

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Reddy, J., and M. Jain. "MIXED EDUCATION MODEL FOR UPGRADING INNOVATIVE ABILITIES AMONG WOMEN." CURRENT RESEARCH JOURNAL OF PEDAGOGICS 03, no. 06 (2022): 7–11. http://dx.doi.org/10.37547/pedagogics-crjp-03-06-02.

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This hypothetical article is committed to the production of Mixed Education Model [MEM] which targets giving a education climate to improving innovative abilities among ladies. Item Improvement process has been utilized for creating MEM. Self-educational methodologies are likewise applied to plan the education circumstance in the MEM. Eye to eye and online method of education are successfully mixed in the MEM which incorporates 70% education through on the web and simply 30% occurs in up close and personal mode. There is a logical course arrangement that has been laid out in various parts of t

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Fuchs, Robin, Denys Pommeret, and Cinzia Viroli. "Mixed Deep Gaussian Mixture Model: a clustering model for mixed datasets." Advances in Data Analysis and Classification 16, no. 1 (2021): 31–53. http://dx.doi.org/10.1007/s11634-021-00466-3.

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Peng, Heng, and Ying Lu. "Model selection in linear mixed effect models." Journal of Multivariate Analysis 109 (August 2012): 109–29. http://dx.doi.org/10.1016/j.jmva.2012.02.005.

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Buscemi, Simona, and Antonella Plaia. "Model selection in linear mixed-effect models." AStA Advances in Statistical Analysis 104, no. 4 (2019): 529–75. http://dx.doi.org/10.1007/s10182-019-00359-z.

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Dreyhaupt, Jens, and Ulrich Mansmann. "S34.1: Model comparison for linear mixed models." Biometrical Journal 46, S1 (2004): 72. http://dx.doi.org/10.1002/bimj.200490125.

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Song, Xin-Yuan, and Sik-Yum Lee. "Model comparison of generalized linear mixed models." Statistics in Medicine 25, no. 10 (2006): 1685–98. http://dx.doi.org/10.1002/sim.2318.

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Ndung’u, A. W., S. Mwalili, and L. Odongo. "Hierarchical Penalized Mixed Model." Open Journal of Statistics 09, no. 06 (2019): 657–63. http://dx.doi.org/10.4236/ojs.2019.96042.

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Housworth, Elizabeth A., Emília P. Martins, and Michael Lynch. "The Phylogenetic Mixed Model." American Naturalist 163, no. 1 (2004): 84–96. http://dx.doi.org/10.1086/380570.

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Dissertations / Theses on the topic "Mixed model"

1

Ribbing, Jakob. "Covariate Model Building in Nonlinear Mixed Effects Models." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-7923.

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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 nonpara

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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 t

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Li, Qie. "A Bayesian Hierarchical Model for Multiple Comparisons in Mixed Models." Bowling Green State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1342530994.

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Prosser, Robert James. "Robustness of multivariate mixed model ANOVA." Thesis, University of British Columbia, 1985. http://hdl.handle.net/2429/25511.

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In experimental or quasi-experimental studies in which a repeated measures design is used, it is common to obtain scores on several dependent variables on each measurement occasion. Multivariate mixed model (MMM) analysis of variance (Thomas, 1983) is a recently developed alternative to the MANOVA procedure (Bock, 1975; Timm, 1980) for testing multivariate hypotheses concerning effects of a repeated factor (called occasions in this study) and interaction between repeated and non-repeated factors (termed group-by-occasion interaction here). If a condition derived by Thomas (1983), multivariate

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Jensen, Willis Aaron. "Profile Monitoring for Mixed Model Data." Diss., Virginia Tech, 2006. http://hdl.handle.net/10919/27054.

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The initial portion of this research focuses on appropriate parameter estimators within a general context of multivariate quality control. The goal of Phase I analysis of multivariate quality control data is to identify multivariate outliers and step changes so that the estimated control limits are sufficiently accurate for Phase II monitoring. High breakdown estimation methods based on the minimum volume ellipsoid (MVE) or the minimum covariance determinant (MCD) are well suited to detecting multivariate outliers in data. Because of the inherent difficulties in computation many algorithms hav

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Wenren, Cheng. "Mixed Model Selection Based on the Conceptual Predictive Statistic." Bowling Green State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1403735738.

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Pan, Juming. "Adaptive LASSO For Mixed Model Selection via Profile Log-Likelihood." Bowling Green State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1466633921.

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Yu, Fu. "On statistical analysis of vehicle time-headways using mixed distribution models." Thesis, University of Dundee, 2014. https://discovery.dundee.ac.uk/en/studentTheses/d101df63-b7db-45b6-8a03-365b64345e6b.

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For decades, vehicle time-headway distribution models have been studied by many researchers and traffic engineers. A good time-headway model can be beneficial to traffic studies and management in many aspects; e.g. with a better understanding of road traffic patterns and road user behaviour, the researchers or engineers can give better estimations and predictions under certain road traffic conditions and hence make better decisions on traffic management and control. The models also help us to implement high-quality microscopic traffic simulation studies to seek good solutions to traffic proble

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Books on the topic "Mixed model"

1

Jiang, Jiming. Robust Mixed Model Analysis. World Scientific Publishing Co Pte Ltd, 2019.

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Jeroslow, Robert G. Logic-based decision support: Mixed integer model formulation. North-Holland, 1989.

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Glen, John J. A mixed integer programming model for fisheries management. University of Edinburgh, Management School, 1994.

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Fhagen-Smith, Peony. Mixed ancestry racial/ethnic identity development (MAREID) model. Center for Research on Women, 2003.

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Hone, David M. Time and space resolution and mixed layer model accuracy. Naval Postgraduate School, 1997.

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Kirches, Christian. Fast Numerical Methods for Mixed-Integer Nonlinear Model-Predictive Control. Vieweg+Teubner Verlag, 2011. http://dx.doi.org/10.1007/978-3-8348-8202-8.

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Murwono, R. P. Djoko. Sistem organik rasional dalam budidaya pangan dengan model mixed farming. Penerbit Universitas Sanata Dharama, 2013.

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Glen, John J. A mixed integer programming model of a single species fishery. Department of Business Studies, University of Edinburgh, 1990.

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Balakrishnan, Anantaram. Setup optimization and workload balancing for mixed-model electronics assembly operations. Alfred P. Sloan School of Management, Massachusetts Institute of Technology, 1993.

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Renssen, Robbert Hans. Robust analysis in a mixed model for the two-way layout. s.n., 1991.

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Book chapters on the topic "Mixed model"

1

Takezawa, Kunio. "Linear Mixed Model." In Learning Regression Analysis by Simulation. Springer Japan, 2013. http://dx.doi.org/10.1007/978-4-431-54321-3_6.

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Buber, Renate, Bernhart Ruso, and Johannes Gadner. "Mixed-Model-Design." In Qualitative Marktforschung. Gabler, 2009. http://dx.doi.org/10.1007/978-3-8349-9441-7_53.

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Plant, Richard E. "The Mixed Model." In Spatial Data Analysis in Ecology and Agriculture Using R. CRC Press, 2018. http://dx.doi.org/10.1201/9781351189910-12.

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Malik, Jamil A., Theresa A. Morgan, Falk Kiefer, et al. "Linear Mixed-Effects Model." In Encyclopedia of Behavioral Medicine. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1005-9_100981.

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Scholl, Armin. "Mixed-Model Assembly Lines." In Balancing and Sequencing of Assembly Lines. Physica-Verlag HD, 1995. http://dx.doi.org/10.1007/978-3-662-00861-4_3.

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Gałecki, Andrzej, and Tomasz Burzykowski. "Linear Mixed-Effects Model." In Springer Texts in Statistics. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3900-4_13.

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Holland, Paul M. "Nonideal Mixed Monolayer Model." In ACS Symposium Series. American Chemical Society, 1986. http://dx.doi.org/10.1021/bk-1986-0311.ch008.

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Edda, Weig. "The Mixed Game Model." In The Routledge Handbook of Language and Dialogue. Routledge, 2017. http://dx.doi.org/10.4324/9781315750583-12.

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Scholl, Armin. "Mixed-Model Assembly Lines." In Balancing and Sequencing of Assembly Lines. Physica-Verlag HD, 1999. http://dx.doi.org/10.1007/978-3-662-11223-6_3.

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Kouvaritakis, Basil, and Mark Cannon. "Robust MPC for Multiplicative and Mixed Uncertainty." In Model Predictive Control. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24853-0_5.

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Conference papers on the topic "Mixed model"

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Gueorguiev, V. G. "Mixed-Mode Shell-Model Calculations." In MAPPING THE TRIANGLE: International Conference on Nuclear Structure. AIP, 2002. http://dx.doi.org/10.1063/1.1517972.

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Barner, Simon, Alexander Diewald, Jorn Migge, et al. "DREAMS Toolchain: Model-Driven Engineering of Mixed-Criticality Systems." In 2017 ACM/IEEE 20th International Conference on Model-Driven Engineering Languages and Systems (MODELS). IEEE, 2017. http://dx.doi.org/10.1109/models.2017.28.

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Wiesmayr, Bianca, Alois Zoitl, and David Hästbacka. "Modeling Service Choreographies and Collaborative Tasks for Autonomous Mixed-Fleet Systems." In MODELS Companion '24: ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems. ACM, 2024. http://dx.doi.org/10.1145/3652620.3686244.

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Gaspar, P. "Iterative model-based mixed H." In UKACC International Conference on Control (CONTROL '98). IEE, 1998. http://dx.doi.org/10.1049/cp:19980306.

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Wang, Hua-kui, Xu-qing Yao, Juan-ping Wu, Ying-zhen Han, and Li-li Feng. "Modulation Recognition Scheme Using Mixed Model." In 2010 First International Conference on Pervasive Computing, Signal Processing and Applications (PCSPA 2010). IEEE, 2010. http://dx.doi.org/10.1109/pcspa.2010.149.

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Liu, Xingxing, Shan Chen, and Pan Wang. "Entropy-Based Mixed Data Transform Model." In 2016 International Conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII). IEEE, 2016. http://dx.doi.org/10.1109/iciicii.2016.0040.

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Odryna, Peter, Kevin Nazareth, and Carl Christensen. "A workstation-mixed model circuit simulator." In the 23rd ACM/IEEE conference. ACM Press, 1986. http://dx.doi.org/10.1145/318013.318043.

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Barba, Evan, and Blair MacIntyre. "A scale model of mixed reality." In the 8th ACM conference. ACM Press, 2011. http://dx.doi.org/10.1145/2069618.2069640.

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Yao, Andrew C. "Agility and mixed-model furniture production." In Intelligent Systems and Smart Manufacturing, edited by Bhaskaran Gopalakrishnan and Angappa Gunasekaran. SPIE, 2000. http://dx.doi.org/10.1117/12.403668.

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Zhang, Yan, Qixia Jiang, and Maosong Sun. "Particle Mixed Membership Stochastic Block Model." In 2012 Eighth International Conference on Semantics, Knowledge and Grids (SKG). IEEE, 2012. http://dx.doi.org/10.1109/skg.2012.39.

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Reports on the topic "Mixed model"

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Buck, Edgar C., and Richard S. Wittman. Radiolysis Model Formulation for Integration with the Mixed Potential Model. Office of Scientific and Technical Information (OSTI), 2014. http://dx.doi.org/10.2172/1168932.

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Harcourt, Ramsey R. Mixed Layer Model Testing in Complex Environments. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada491727.

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Palmer, B. A. Mixed waste treatment model: Basis and analysis. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/113762.

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Plueddemann, Albert J., Robert A. Weller, and James F. Price. A Mixed Layer Model with Surface Wave Forcing. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada324887.

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Michael E. Mullins, Tony N. Rogers, Stephanie L. Outcalt, Beverly Louie, Laurel A. Watts, and Cynthia D. Holcomb. Measurement and Model for Hazardous Chemical and Mixed Waste. Office of Scientific and Technical Information (OSTI), 2002. http://dx.doi.org/10.2172/799235.

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Burda, Martin, Matthew C. Harding, and Jerry Hausman. A Bayesian mixed logit-probit model for multinomial choice. Institute for Fiscal Studies, 2008. http://dx.doi.org/10.1920/wp.cem.2008.2308.

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Buck, Edgar C., and Richard S. Wittman. Addition of Bromide to Radiolysis Model Formulation for Integration with the Mixed Potential Model. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1592703.

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Hartley, III, D. S., and K. L. Kruse. Historical support for a mixed law Lanchestrian Attrition Model: Helmbold's ratio. Office of Scientific and Technical Information (OSTI), 1989. http://dx.doi.org/10.2172/5087936.

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Rossi, R., B. Gallagher, J. Neville, and K. Henderson. Modeling Temporal Behavior in Large Networks: A Dynamic Mixed-Membership Model. Office of Scientific and Technical Information (OSTI), 2011. http://dx.doi.org/10.2172/1035597.

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Wang, Zhien. Improving Mixed-phase Cloud Parameterization in Climate Model with the ACRF Measurements. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1335565.

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