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Journal articles on the topic 'Mixed model'

Author: Grafiati

Published: 4 June 2021

Last updated: 20 April 2024

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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 (May 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 (June 1, 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 the MEM like the points and targets, responsibilities of student, content and setting, education results and human cooperations, communications with content, situations based education for establishing relevant education climate, and so on. Self-administered education exercises have been created with the end goal of viable education.

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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 (October 6, 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 (October 28, 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 (March 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

Adamec, Z. "Comparison of linear mixed effects model and generalized model of the tree height-diameter relationship." Journal of Forest Science 61, No. 10 (June 3, 2016): 439–47. http://dx.doi.org/10.17221/68/2015-jfs.

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10

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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11

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

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12

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

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13

Jiang, Jiming, J. Sunil Rao, Jie Fan, and Thuan Nguyen. "Classified Mixed Model Prediction." Journal of the American Statistical Association 113, no. 521 (September 26, 2017): 269–79. http://dx.doi.org/10.1080/01621459.2016.1246367.

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14

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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15

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

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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 (October 31, 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 (May 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 (December 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 (August 23, 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 (November 5, 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 (July 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 the logical relationship and temporal relationship between the nodes, respectively. To run the standard BN inference algorithm over TBN, we fit the failure distribution of the nodes by using k-piece and n-degree polynomials. We then propose a transformation method from DFT to TBN and prove the equivalence of the transformation. Finally, the analysis of the DFT model of the X2000 avionics system shows that our approach can effectively analyze the reliability of mixed distribution failure models. Moreover, the accuracy and efficiency of the analysis are significantly better than current mainstream methods.</p> <p> </p>

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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 (June 30, 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 (December 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 (May 20, 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 gives only second-order phase transitions in addition to the compensation temperatures.

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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 (June 21, 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 (January 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 (December 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 (July 19, 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 interpreted as a rate variation process undergoing short-term fluctuations on the top of Brownian long-term trends. Statistically, this mixed clock represents an alternative solution to the problem of choosing between autocorrelated and uncorrelated relaxed clocks, by proposing instead to combine their respective merits. Fitting this model on a dataset of 105 placental mammals, using both node-dating and tip-dating approaches, suggests that the two pure clocks, Brownian and white noise, are rejected in favour of a mixed model with approximately equal contributions for its uncorrelated and autocorrelated components. The tip-dating analysis is particularly sensitive to the choice of the relaxed clock model. In this context, the classical pure Brownian relaxed clock appears to be overly rigid, leading to biases in divergence time estimation. By contrast, the use of a mixed clock leads to more recent and more reasonable estimates for the crown ages of placental orders and superorders. Altogether, the mixed clock introduced here represents a first step towards empirically more adequate models of the patterns of rate variation across phylogenetic trees. This article is part of the themed issue ‘Dating species divergences using rocks and clocks’.

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29

Rosenthal, Eric T. "Mixed Missions Muddle Matrix Model." Oncology Times 29, no. 24 (December 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 (July 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 (February 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 (July 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 (May 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 (January 30, 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, depending on the old density. The symmetry breaking requires certain eigenvalue condition, which takes some extra parameter adjustment of the scalar potential.

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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 (July 13, 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 (July 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 (July 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 (June 15, 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 (August 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 (February 24, 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 (July 14, 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 (May 2, 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 this article, we propose a modified BIC for model selection with linear mixed-effects models that can solve the case when the variance components are on the boundary of the parameter space. Through the simulation results, we found that the modified BIC performed better than the regular BIC in most cases for linear mixed models. The modified BIC was also applied to a real dataset to choose the most-appropriate model.

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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

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 (September 30, 2020): 783–93. http://dx.doi.org/10.7465/jkdi.2020.31.5.783.

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48

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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49

Hlaváček, Ivan. "Reissner-Mindlin model for plates of variable thickness. Solution by mixed-interpolated elements." Applications of Mathematics 41, no. 1 (1996): 57–78. http://dx.doi.org/10.21136/am.1996.134313.

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50

Hlaváček, Ivan. "Weight minimization of elastic plates using Reissner-Mindlin model and mixed-interpolated elements." Applications of Mathematics 41, no. 2 (1996): 107–21. http://dx.doi.org/10.21136/am.1996.134316.

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