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Journal articles on the topic 'Mixed model'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Carlson, John G., and Andrew C. Yao. "Mixed model assembly simulation." International Journal of Production Economics 26, no. 1-3 (1992): 161–67. http://dx.doi.org/10.1016/0925-5273(92)90058-f.

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12

Jiang, Jiming, J. Sunil Rao, Jie Fan, and Thuan Nguyen. "Classified Mixed Model Prediction." Journal of the American Statistical Association 113, no. 521 (2017): 269–79. http://dx.doi.org/10.1080/01621459.2016.1246367.

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13

Zhaorong, J., C. A. McGilchrist, and M. A. Jorgensen. "Mixed Model Discrete Regression." Biometrical Journal 34, no. 6 (1992): 691–700. http://dx.doi.org/10.1002/bimj.4710340607.

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14

Westerlund, Joakim, Mattias Hästbacka, Sebastian Forssell, and Tapio Westerlund. "Mixed-Time Mixed-Integer Linear Programming Scheduling Model." Industrial & Engineering Chemistry Research 46, no. 9 (2007): 2781–96. http://dx.doi.org/10.1021/ie060991a.

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15

Adamec, Z. "Comparison of linear mixed effects model and generalized model of the tree height-diameter relationship." Journal of Forest Science 61, No. 10 (2016): 439–47. http://dx.doi.org/10.17221/68/2015-jfs.

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16

RP, Sharma, Z. Vacek, and S. Vacek. "Nonlinear mixed effect height-diameter model for mixed species forests in the central part of the Czech Republic." Journal of Forest Science 62, No. 10 (2016): 470–84. http://dx.doi.org/10.17221/41/2016-jfs.

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17

Qu, Xianggui. "Linear Model Theory: Univariate, Multivariate, and Mixed Models." Technometrics 49, no. 2 (2007): 223. http://dx.doi.org/10.1198/tech.2007.s478.

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18

Padoan, S. A., and M. P. Wand. "Mixed model-based additive models for sample extremes." Statistics & Probability Letters 78, no. 17 (2008): 2850–58. http://dx.doi.org/10.1016/j.spl.2008.04.009.

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19

Sciandra, M., and A. Plaia. "A graphical model selection tool for mixed models." Communications in Statistics - Simulation and Computation 47, no. 9 (2017): 2624–38. http://dx.doi.org/10.1080/03610918.2017.1353617.

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20

Chen, Xinjie, Guohua Zou, and Xinyu Zhang. "Frequentist model averaging for linear mixed-effects models." Frontiers of Mathematics in China 8, no. 3 (2012): 497–515. http://dx.doi.org/10.1007/s11464-012-0254-x.

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21

Li Wei, Li Wei, and Bing-Wu Fang Li Wei. "Safety Analysis Method of Mixed Failure Model using Temporal Bayesian Network." 網際網路技術學刊 23, no. 4 (2022): 727–34. http://dx.doi.org/10.53106/160792642022072304008.

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<p>Dynamic Fault Tree (DFT) is used widely in the community of reliability and safety analysis of a complex system. DFT is a high-level modeling language lacking formal semantics, so we need to convert it to a mathematical model to analyze. The conventional analysis method can only solve the DFT with discrete or exponential distribution, but not the DFT with mixed distributions. To this end, we first propose a TBN framework to represent the DFT with mixed failure distribution by extending the BN and introduce Dirac delta functions and unit-step functions into the framework to represent t

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22

Serna-Diaz, Raquel, Raimundo Santos Leite, and Paulo J. S. Silva. "A mixed quadratic programming model for a robust support vector machine." Selecciones Matemáticas 8, no. 1 (2021): 27–36. http://dx.doi.org/10.17268/sel.mat.2021.01.03.

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23

Larsson, Sven. "Mixed valence model for superconductivity." Brazilian Journal of Physics 33, no. 4 (2003): 744–49. http://dx.doi.org/10.1590/s0103-97332003000400022.

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24

Albayrak, Erhan. "Triple mixed-spin Ising model." International Journal of Modern Physics B 34, no. 13 (2020): 2050129. http://dx.doi.org/10.1142/s0217979220501295.

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The A, B and C atoms with spin-1/2, spin-3/2 and spin-5/2 are joined together sequentially on the Bethe lattice in the form of ABCABC[Formula: see text] to simulate a molecule as a triple mixed-spin system. The spins are assumed to be interacting with only their nearest-neighbors via bilinear exchange interaction parameter in addition to crystal and external magnetic fields. The order-parameters are obtained in terms of exact recursion relations, then from the study of their thermal variations, the phase diagrams are calculated on the possible planes of our system. It is found that the model g

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25

Ristić, Miroslav M., and Aleksandar S. Nastić. "A mixed INAR(p) model." Journal of Time Series Analysis 33, no. 6 (2012): 903–15. http://dx.doi.org/10.1111/j.1467-9892.2012.00806.x.

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26

Patel, S. R., and N. P. Patel. "Mixed effect exponential linear model." Communications in Statistics - Theory and Methods 21, no. 9 (1992): 2721–40. http://dx.doi.org/10.1080/03610929208830939.

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27

Cai, T., R. J. Hyndman, and M. P. Wand. "Mixed Model-Based Hazard Estimation." Journal of Computational and Graphical Statistics 11, no. 4 (2002): 784–98. http://dx.doi.org/10.1198/106186002862.

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28

Lartillot, Nicolas, Matthew J. Phillips, and Fredrik Ronquist. "A mixed relaxed clock model." Philosophical Transactions of the Royal Society B: Biological Sciences 371, no. 1699 (2016): 20150132. http://dx.doi.org/10.1098/rstb.2015.0132.

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Over recent years, several alternative relaxed clock models have been proposed in the context of Bayesian dating. These models fall in two distinct categories: uncorrelated and autocorrelated across branches. The choice between these two classes of relaxed clocks is still an open question. More fundamentally, the true process of rate variation may have both long-term trends and short-term fluctuations, suggesting that more sophisticated clock models unfolding over multiple time scales should ultimately be developed. Here, a mixed relaxed clock model is introduced, which can be mechanistically

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29

Rosenthal, Eric T. "Mixed Missions Muddle Matrix Model." Oncology Times 29, no. 24 (2007): 26. http://dx.doi.org/10.1097/01.cot.0000305575.03295.f6.

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30

Huang, Poshu, Joseph L. DiLorenzo, and Tavit O. Najarian. "Mixed‐Layer Hydrothermal Reservoir Model." Journal of Hydraulic Engineering 120, no. 7 (1994): 846–62. http://dx.doi.org/10.1061/(asce)0733-9429(1994)120:7(846).

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31

Baltagi, Badi H., and Walter Krämer. "A Mixed-Error Component Model." Econometric Theory 11, no. 1 (1995): 192–93. http://dx.doi.org/10.1017/s0266466600009117.

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32

Sun, Hanmei, Thuan Nguyen, Yihui Luan, and Jiming Jiang. "Classified mixed logistic model prediction." Journal of Multivariate Analysis 168 (November 2018): 63–74. http://dx.doi.org/10.1016/j.jmva.2018.06.004.

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33

Kim, Jong-Min, and William D. Warde. "A mixed randomized response model." Journal of Statistical Planning and Inference 133, no. 1 (2005): 211–21. http://dx.doi.org/10.1016/j.jspi.2004.03.011.

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34

Vedavathi, B., and B. Muniswamy. "Mixed Model Analysis for Overdispersion." International Journal of Engineering and Science 6, no. 05 (2017): 07–15. http://dx.doi.org/10.9790/1813-0605020715.

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35

MIGDAL, A. A. "MIXED MODEL OF INDUCED QCD." Modern Physics Letters A 08, no. 03 (1993): 245–58. http://dx.doi.org/10.1142/s0217732393000258.

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The problems with the ZN symmetry breaking in the induced QCD are analyzed. We compute the Wilson loops in the strong coupling phase, but we do not find the ZN symmetry breaking, for arbitrary potential. We suggest to bypass this problem by adding to the model a heavy fermion field in a fundamental representation of SU(N). Remarkably, the model can still be solved exactly by the Riemann-Hilbert method, for arbitrary number of flavors, Nf. At Nf≪N→∞ there is a new regime, with two vacuum densities. The ZN symmetry breaking density satisfies the linear integral equation, with the kernel, dependi

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36

Mandt, Stephan, Florian Wenzel, Shinichi Nakajima, John Cunningham, Christoph Lippert, and Marius Kloft. "Sparse probit linear mixed model." Machine Learning 106, no. 9-10 (2017): 1621–42. http://dx.doi.org/10.1007/s10994-017-5652-6.

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37

Fernando, R. L., C. Stricker, and R. C. Elston. "The finite polygenic mixed model: An alternative formulation for the mixed model of inheritance." Theoretical and Applied Genetics 88, no. 5 (1994): 573–80. http://dx.doi.org/10.1007/bf01240920.

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38

Mohaisen, Ameera Jaber, and Saja Yaseen Abdulsamad. "BAYESIAN PANEL DATA MODEL AS A MIXED MODEL." Far East Journal of Mathematical Sciences (FJMS) 104, no. 1 (2018): 27–47. http://dx.doi.org/10.17654/ms104010027.

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39

Tamási, Bálint, and Torsten Hothorn. "tramME: Mixed-Effects Transformation Models Using Template Model Builder." R Journal 13, no. 2 (2021): 306. http://dx.doi.org/10.32614/rj-2021-075.

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40

Kruse, René-Marcel, Alexander Silbersdorff, and Benjamin Säfken. "Model averaging for linear mixed models via augmented Lagrangian." Computational Statistics & Data Analysis 167 (March 2022): 107351. http://dx.doi.org/10.1016/j.csda.2021.107351.

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41

Preinerstorfer, David, and Anton K. Formann. "Parameter recovery and model selection in mixed Rasch models." British Journal of Mathematical and Statistical Psychology 65, no. 2 (2011): 251–62. http://dx.doi.org/10.1111/j.2044-8317.2011.02020.x.

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42

Piepho, Hans-Peter, and Joseph Ochieng Ogutu. "Simple State-Space Models in a Mixed Model Framework." American Statistician 61, no. 3 (2007): 224–32. http://dx.doi.org/10.1198/000313007x220426.

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43

Wang, Jun, and G. Bruce Schaalje. "Model Selection for Linear Mixed Models Using Predictive Criteria." Communications in Statistics - Simulation and Computation 38, no. 4 (2009): 788–801. http://dx.doi.org/10.1080/03610910802645362.

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44

Lavergne, Christian, Marie-José Martinez, and Catherine Trottier. "Empirical model selection in generalized linear mixed effects models." Computational Statistics 23, no. 1 (2007): 99–109. http://dx.doi.org/10.1007/s00180-007-0071-y.

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45

Lai, Hang, and Xin Gao. "Modified BIC Criterion for Model Selection in Linear Mixed Models." Mathematics 11, no. 9 (2023): 2130. http://dx.doi.org/10.3390/math11092130.

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Linear mixed-effects models are widely used in applications to analyze clustered, hierarchical, and longitudinal data. Model selection in linear mixed models is more challenging than that of linear models as the parameter vector in a linear mixed model includes both fixed effects and variance component parameters. When selecting the variance components of the random effects, the variance of the random effects must be non-negative and the parameters may lie on the boundary of the parameter space. Therefore, classical model selection methods cannot be directly used to handle this situation. In t

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46

Funatogawa, Ikuko, and Takashi Funatogawa. "Fundamentals in Population Pharmacokinetics: Mathematics in Linear Mixed Effects Model and Nonlinear Mixed Effects Model." Japanese Journal of Biometrics 36, Special_Issue (2015): S33—S48. http://dx.doi.org/10.5691/jjb.36.s33.

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47

Marmanis, Iason, Michalis Kokologiannakis, and Viktor Vafeiadis. "Model Checking C/C++ with Mixed-Size Accesses." Proceedings of the ACM on Programming Languages 9, POPL (2025): 2232–52. https://doi.org/10.1145/3704911.

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State-of-the-art model checkers employing dynamic partial order reduction (DPOR) can verify concurrent programs under a wide range of memory models such as sequential consistency (SC), total store order (TSO), release-acquire (RA), and the repaired C11 memory model (RC11) in an optimal and memory-efficient fashion. Unfortunately, these DPOR techniques cannot be applied in an optimal fashion to programs with mixed-sized accesses (MSA), where atomic instructions access different (sets of) bytes belonging to the same word. Such patterns naturally arise in real life code with C/C++ union types, an

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48

Kim, Jieun, and Jong Hwa Na. "Cerebrovascular disease prediction model using generalized linear mixed model." Journal of the Korean Data And Information Science Society 31, no. 5 (2020): 783–93. http://dx.doi.org/10.7465/jkdi.2020.31.5.783.

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49

Miyamoto, Michiko, and Hiroe Tsubaki. "A linear mixed model for the hedonic pricing model." Applied Stochastic Models in Business and Industry 18, no. 3 (2002): 259–70. http://dx.doi.org/10.1002/asmb.470.

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50

Zhang, Yicai, Min Zhao, Dihua Sun, and Chen Dong. "An extended continuum mixed traffic model." Nonlinear Dynamics 103, no. 2 (2021): 1891–909. http://dx.doi.org/10.1007/s11071-021-06201-z.

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