Journal articles: 'Meta Models'

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Wheeler, Gregory. "Models, Models, and Models." Metaphilosophy 44, no. 3 (April 2013): 293–300. http://dx.doi.org/10.1111/meta.12036.

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Ehrig, Karsten, Jochen Malte Küster, and Gabriele Taentzer. "Generating instance models from meta models." Software & Systems Modeling 8, no. 4 (July 1, 2008): 479–500. http://dx.doi.org/10.1007/s10270-008-0095-y.

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Shenton, Andrew K., and Naomi V. Hay‐Gibson. "Information behaviour meta‐models." Library Review 61, no. 2 (February 24, 2012): 92–109. http://dx.doi.org/10.1108/00242531211220735.

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Elfaham, Haitham, and Ulrich Epple. "Meta models for intralogistics." at - Automatisierungstechnik 68, no. 3 (March 26, 2020): 208–21. http://dx.doi.org/10.1515/auto-2019-0083.

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AbstractIn industrial automation, adaptability is a key feature that can enhance the degree of autonomy in a plant sparing engineering months and costs. With the introduction of the service oriented architecture in control automation, a topology model is required to identify the vicinity relationships between the devices. Nowadays, due to the absence of the coupling between logistic aspects and control logic, the topology models are manually constructed which consequently affects the autonomy of the procedures generation. In this contribution, we introduce a concept to couple the logistics model with the devices abilities to generate product flow paths and procedures considering adaptable conditions.

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Conn, Vicki S. "Meta-Analysis for Models." Western Journal of Nursing Research 37, no. 4 (February 25, 2015): 423–24. http://dx.doi.org/10.1177/0193945914563305.

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Babenyshev, S., and V. Rybakov. "Logics of Kripke meta-models." Logic Journal of IGPL 18, no. 6 (September 3, 2009): 823–36. http://dx.doi.org/10.1093/jigpal/jzp047.

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Ma, Han, Baoyu Fan, Benjamin K. Ng, and Chan-Tong Lam. "VL-Meta: Vision-Language Models for Multimodal Meta-Learning." Mathematics 12, no. 2 (January 16, 2024): 286. http://dx.doi.org/10.3390/math12020286.

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Multimodal learning is a promising area in artificial intelligence (AI) that can make the model understand different kinds of data. Existing works are trying to re-train a new model based on pre-trained models that requires much data, computation power, and time. However, it is difficult to achieve in low-resource or small-sample situations. Therefore, we propose VL-Meta, Vision Language Models for Multimodal Meta Learning. It (1) presents the vision-language mapper and multimodal fusion mapper, which are light model structures, to use the existing pre-trained models to make models understand images to language feature space and save training data, computation power, and time; (2) constructs the meta-task pool that can only use a small amount of data to construct enough training data and improve the generalization of the model to learn the data knowledge and task knowledge; (3) proposes the token-level training that can align inputs with the outputs during training to improve the model performance; and (4) adopts the multi-task fusion loss to learn the different abilities for the models. It achieves a good performance on the Visual Question Answering (VQA) task, which shows the feasibility and effectiveness of the model. This solution can help blind or visually impaired individuals obtain visual information.

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Patterson, Eann. "On the credibility of engineering models and meta-models." Journal of Strain Analysis for Engineering Design 50, no. 4 (April 14, 2015): 218–20. http://dx.doi.org/10.1177/0309324715577930.

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Sheu, Ching-Fan, and Sawako Suzuki. "Meta-analysis using linear mixed models." Behavior Research Methods, Instruments, & Computers 33, no. 2 (May 2001): 102–7. http://dx.doi.org/10.3758/bf03195354.

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Bruns, Stephan B. "Meta-Regression Models and Observational Research." Oxford Bulletin of Economics and Statistics 79, no. 5 (March 22, 2017): 637–53. http://dx.doi.org/10.1111/obes.12172.

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Gross, Thilo, Korinna T. Allhoff, Bernd Blasius, Ulrich Brose, Barbara Drossel, Ashkaan K. Fahimipour, Christian Guill, Justin D. Yeakel, and Fanqi Zeng. "Modern models of trophic meta-communities." Philosophical Transactions of the Royal Society B: Biological Sciences 375, no. 1814 (November 2, 2020): 20190455. http://dx.doi.org/10.1098/rstb.2019.0455.

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Dispersal and foodweb dynamics have long been studied in separate models. However, over the past decades, it has become abundantly clear that there are intricate interactions between local dynamics and spatial patterns. Trophic meta-communities, i.e. meta-foodwebs, are very complex systems that exhibit complex and often counterintuitive dynamics. Over the past decade, a broad range of modelling approaches have been used to study these systems. In this paper, we review these approaches and the insights that they have revealed. We focus particularly on recent papers that study trophic interactions in spatially extensive settings and highlight the common themes that emerged in different models. There is overwhelming evidence that dispersal (and particularly intermediate levels of dispersal) benefits the maintenance of biodiversity in several different ways. Moreover, some insights have been gained into the effect of different habitat topologies, but these results also show that the exact relationships are much more complex than previously thought, highlighting the need for further research in this area. This article is part of the theme issue ‘Integrative research perspectives on marine conservation’.

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Gallegos, Arnold. "Meta-evaluation of school evaluation models." Studies in Educational Evaluation 20, no. 1 (January 1994): 41–54. http://dx.doi.org/10.1016/s0191-491x(00)80004-8.

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Harald, Baumeister, and Parker Gordon. "Meta-review of depressive subtyping models." Journal of Affective Disorders 139, no. 2 (July 2012): 126–40. http://dx.doi.org/10.1016/j.jad.2011.07.015.

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Allen, Theodore T., Mikhail A. Bernshteyn, and Khalil Kabiri-Bamoradian. "Constructing Meta-Models for Computer Experiments." Journal of Quality Technology 35, no. 3 (July 2003): 264–74. http://dx.doi.org/10.1080/00224065.2003.11980220.

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Dumouchel, William. "Meta-analysis for dose—response models." Statistics in Medicine 14, no. 5-7 (March 15, 1995): 679–85. http://dx.doi.org/10.1002/sim.4780140524.

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Hedges, Larry V. "Meta-Analysis." Journal of Educational Statistics 17, no. 4 (December 1992): 279–96. http://dx.doi.org/10.3102/10769986017004279.

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The use of statistical methods to combine the results of independent empirical research studies (meta-analysis) has a long history. Meta-analytic work can be divided into two traditions: tests of the statistical significance of combined results and methods for combining estimates across studies. The principal classes of combined significance tests are reviewed, and the limitations of these tests are discussed. Fixed effects approaches treat the effect magnitude parameters to be estimated as a consequence of a model involving fixed but unknown constants. Random effects approaches treat effect magnitude parameters as if they were sampled from a universe of effects and attempt to estimate the mean and variance of the hyperpopulation of effects. Mixed models incorporate both fixed and random effects. Finally, areas of current research are summarized, including methods for handling missing data, models for publication selection, models to handle studies that are not independent, and distribution-free models for random effects.

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Ehrig, Hartmut, Karsten Ehrig, Claudia Ermel, and Ulrike Prange. "Consistent integration of models based on views of meta models." Formal Aspects of Computing 22, no. 3 (October 2, 2009): 327–44. http://dx.doi.org/10.1007/s00165-009-0127-6.

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Bonissone, Piero P., Feng Xue, and Raj Subbu. "Fast meta-models for local fusion of multiple predictive models." Applied Soft Computing 11, no. 2 (March 2011): 1529–39. http://dx.doi.org/10.1016/j.asoc.2008.03.006.

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Palmer, Kent D. "9.3.2 Vajra Logic and Mathematical Meta-models for Meta-systems Engineering." INCOSE International Symposium 12, no. 1 (August 2002): 475–86. http://dx.doi.org/10.1002/j.2334-5837.2002.tb02499.x.

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Han, Xinyu, Xinya Wang, Xiaotong Chen, Hong Liu, Jingwen Liu, Mary Miu Yee Waye, Guangming Liu, and Shitao Rao. "Intervention Efficacy of Slightly Processed Allergen/Meat in Oral Immunotherapy for Seafood Allergy: A Systematic Review, Meta-Analysis, and Meta-Regression Analysis in Mouse Models and Clinical Patients." Nutrients 16, no. 5 (February 27, 2024): 667. http://dx.doi.org/10.3390/nu16050667.

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Background: Seafood allergy is a significant global health concern that greatly impacts a patient’s quality of life. The intervention efficacy of oral immunotherapy (OIT), an emerging intervention strategy, for seafood allergy remains controversial. This study aimed to perform a systematic review and meta-analysis to evaluate the efficacy of slightly processed allergen/meat from fish and crustacea in OIT, both in mouse models and clinical patients. Methods: A comprehensive literature search was performed in four mainstream databases and the EBSCOhost database to identify all relevant case–control and cohort studies. The aim was to elucidate the intervention efficacy, encompassing various processing methods and assessing the efficacy of multiple major allergens in OIT. Results: The meta-analysis included five case–control studies on crustacean allergens in mouse models and 11 cohort studies on meat from fish and crustacea in clinical patients for final quantitative assessments. In mouse models, crustacean allergen substantially decreased the anaphylactic score after OIT treatment (mean difference (MD) = −1.30, p < 0.01). Subgroup analyses with low-level heterogeneities provided more reliable results for crab species (MD = −0.63, p < 0.01, I2 = 0), arginine kinase allergen (MD = −0.83, p < 0.01, I2 = 0), and Maillard reaction processing method (MD = −0.65, p < 0.01, I2 = 29%), respectively. In clinical patients, the main meta-analysis showed that the slightly processed meat significantly increased the incidence rate of oral tolerance (OT, incidence rate ratio (IRR) = 2.90, p < 0.01). Subgroup analyses for fish meat (IRR = 2.79, p < 0.01) and a simple cooking treatment (IRR = 2.36, p = 0.01) also demonstrated a substantial increase in the incidence rate of OT. Sensitivity and meta-regression analyses successfully identified specific studies contributing to heterogeneity in mouse models and clinical patients, although these studies did not impact the overall significant pooled effects. Conclusions: This meta-analysis provides preliminary evidence for the high intervention efficacy of slightly processed allergen/meat from fish and crustacea in OIT, both in mouse models and clinical patients. The Maillard reaction and cooking processing methods may emerge as potentially effective approaches to treating allergen/meat in OIT for clinical patients, offering a promising and specific treatment strategy for seafood allergy. However, these findings should be interpreted cautiously, and further supporting evidence is necessary.

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Alicea, B., and J. Parent. "Meta-brain Models: biologically-inspired cognitive agents." IOP Conference Series: Materials Science and Engineering 1261, no. 1 (October 1, 2022): 012019. http://dx.doi.org/10.1088/1757-899x/1261/1/012019.

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Abstract Artificial Intelligence (AI) systems based solely on neural networks or symbolic computation present a representational complexity challenge. While minimal representations can produce behavioral outputs like locomotion or simple decision-making, more elaborate internal representations might offer a richer variety of behaviors. We propose that these issues can be addressed with a computational approach we call meta-brain models. Meta-brain models are embodied hybrid models that include layered components featuring varying degrees of representational complexity. We will propose combinations of layers composed using specialized types of models. Rather than using a generic black box approach to unify each component, this relationship mimics systems like the neocortical-thalamic system relationship of the mammalian brain, which utilizes both feedforward and feedback connectivity to facilitate functional communication. Importantly, the relationship between layers can be made anatomically explicit. This allows for structural specificity that can be incorporated into the model's function in interesting ways. We will propose several types of layers that might be functionally integrated into agents that perform unique types of tasks, from agents that simultaneously perform morphogenesis and perception, to agents that undergo morphogenesis and the acquisition of conceptual representations simultaneously. Our approach to meta-brain models involves creating models with different degrees of representational complexity, creating a layered meta-architecture that mimics the structural and functional heterogeneity of biological brains, and an input/output methodology flexible enough to accommodate cognitive functions, social interactions, and adaptive behaviors more generally. We will conclude by proposing next steps in the development of this flexible and open-source approach.

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Madden, L. V., H. P. Piepho, and P. A. Paul. "Statistical Models and Methods for Network Meta-Analysis." Phytopathology® 106, no. 8 (August 2016): 792–806. http://dx.doi.org/10.1094/phyto-12-15-0342-rvw.

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Meta-analysis, the methodology for analyzing the results from multiple independent studies, has grown tremendously in popularity over the last four decades. Although most meta-analyses involve a single effect size (summary result, such as a treatment difference) from each study, there are often multiple treatments of interest across the network of studies in the analysis. Multi-treatment (or network) meta-analysis can be used for simultaneously analyzing the results from all the treatments. However, the methodology is considerably more complicated than for the analysis of a single effect size, and there have not been adequate explanations of the approach for agricultural investigations. We review the methods and models for conducting a network meta-analysis based on frequentist statistical principles, and demonstrate the procedures using a published multi-treatment plant pathology data set. A major advantage of network meta-analysis is that correlations of estimated treatment effects are automatically taken into account when an appropriate model is used. Moreover, treatment comparisons may be possible in a network meta-analysis that are not possible in a single study because all treatments of interest may not be included in any given study. We review several models that consider the study effect as either fixed or random, and show how to interpret model-fitting output. We further show how to model the effect of moderator variables (study-level characteristics) on treatment effects, and present one approach to test for the consistency of treatment effects across the network. Online supplemental files give explanations on fitting the network meta-analytical models using SAS.

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Riley, Richard D., Joie Ensor, Dan Jackson, and Danielle L. Burke. "Deriving percentage study weights in multi-parameter meta-analysis models: with application to meta-regression, network meta-analysis and one-stage individual participant data models." Statistical Methods in Medical Research 27, no. 10 (February 6, 2017): 2885–905. http://dx.doi.org/10.1177/0962280216688033.

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Many meta-analysis models contain multiple parameters, for example due to multiple outcomes, multiple treatments or multiple regression coefficients. In particular, meta-regression models may contain multiple study-level covariates, and one-stage individual participant data meta-analysis models may contain multiple patient-level covariates and interactions. Here, we propose how to derive percentage study weights for such situations, in order to reveal the (otherwise hidden) contribution of each study toward the parameter estimates of interest. We assume that studies are independent, and utilise a decomposition of Fisher’s information matrix to decompose the total variance matrix of parameter estimates into study-specific contributions, from which percentage weights are derived. This approach generalises how percentage weights are calculated in a traditional, single parameter meta-analysis model. Application is made to one- and two-stage individual participant data meta-analyses, meta-regression and network (multivariate) meta-analysis of multiple treatments. These reveal percentage study weights toward clinically important estimates, such as summary treatment effects and treatment-covariate interactions, and are especially useful when some studies are potential outliers or at high risk of bias. We also derive percentage study weights toward methodologically interesting measures, such as the magnitude of ecological bias (difference between within-study and across-study associations) and the amount of inconsistency (difference between direct and indirect evidence in a network meta-analysis).

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Copas, J. "What works?: selectivity models and meta‐analysis." Journal of the Royal Statistical Society: Series A (Statistics in Society) 162, no. 1 (January 1999): 95–109. http://dx.doi.org/10.1111/1467-985x.00123.

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Snowdon, Bob, and Peter Kawalek. "Active meta-process models: a conceptual exposition." Information and Software Technology 45, no. 15 (December 2003): 1021–29. http://dx.doi.org/10.1016/s0950-5849(03)00130-7.

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Ristic, Sonja, Slavica Aleksic, Milan Celikovic, and Ivan Lukovic. "Generic and standard database constraint meta-models." Computer Science and Information Systems 11, no. 2 (2014): 679–96. http://dx.doi.org/10.2298/csis140216037r.

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Many software engineering activities entail dealing with legacy information systems. When these systems become too costly to maintain, or when new technologies need to be incorporated, they need to be replaced or somehow reengineered. This can be done with significantly reduced amount of effort and cost if the conceptual models of these systems are available. Reverse engineering is the process of analyzing a subject system to create representations of the system at a higher level of abstraction. Relational databases are a common source of reverse engineering. Starting from a physical database schema, that is recorded into relational database schema data repository, the conceptual database schema or logical database schema could be extracted. The extraction process may be seen as a chain of model-to-model transformations that trace model elements from a model at the lower level of abstraction to a model at the higher level of abstraction, achieved through meta-modeling. In the paper we present generic and standard database constraint meta-models, focusing on multi-relational database constraints captured in a legacy database. These meta-models are aimed at support of model transformations to create conceptual models, as a useful source for the system reengineering process.

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Thiry, Laurent, and Michel Hassenforder. "A Calculus for (Meta)Models and Transformations." International Journal of Software Engineering and Knowledge Engineering 24, no. 05 (June 2014): 715–30. http://dx.doi.org/10.1142/s0218194014500272.

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This paper proposes a formal representation of modeling languages based on category theory. These languages are generally described by "metamodels", i.e. structures composed by classes and relations, and related by "transformations". Thus, this paper studies how the key categorical concepts such as functors and relations between functors (called natural transformations) can be used for equational reasoning about modeling artifacts (models, metamodels, transformations). As a result, this paper proposes a formal point of view of models usable to specify/prove equivalence between models or transformations (with an application to refactoring).

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Cheung, Mike W. L. "Multivariate Meta-Analysis as Structural Equation Models." Structural Equation Modeling: A Multidisciplinary Journal 20, no. 3 (July 1, 2013): 429–54. http://dx.doi.org/10.1080/10705511.2013.797827.

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Eusebi, P., J. B. Reitsma, and J. K. Vermunt. "On mixture models for diagnostic meta-analyses." Journal of Clinical Epidemiology 68, no. 12 (December 2015): 1523. http://dx.doi.org/10.1016/j.jclinepi.2015.01.020.

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Platt, Robert W., Brian G. Leroux, and Norman Breslow. "Generalized linear mixed models for meta-analysis." Statistics in Medicine 18, no. 6 (March 30, 1999): 643–54. http://dx.doi.org/10.1002/(sici)1097-0258(19990330)18:6<643::aid-sim76>3.0.co;2-m.

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Eriksson, Henrik, and Mark A. Musen. "Conceptual models for automatic generation of knowledge-acquisition tools." Knowledge Engineering Review 8, no. 1 (March 1993): 27–47. http://dx.doi.org/10.1017/s0269888900000059.

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AbstractInteractive knowledge-acquisition (KA) programs allow users to enter relevant domain knowledge according to a model predefined by the tool developers. KA tools are designed to provide conceptual models of the knowledge to their users. Many different classes of models are possible, resulting in different categories of tools. Whenever it is possible to describe KA tools according to explicit conceptual models, it is also possible to edit the models and to instantiate new KA tools automatically for specialized purposes. Several meta-tools that address this task have been implemented. Meta-tools provide developers of domain-specific KA tools with generic design models, or meta-views, of the emerging KA tools. The same KA tool can be specified according to several alternative meta-views.

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Simmonds, Mark C., and Julian PT Higgins. "A general framework for the use of logistic regression models in meta-analysis." Statistical Methods in Medical Research 25, no. 6 (July 11, 2016): 2858–77. http://dx.doi.org/10.1177/0962280214534409.

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Where individual participant data are available for every randomised trial in a meta-analysis of dichotomous event outcomes, “one-stage” random-effects logistic regression models have been proposed as a way to analyse these data. Such models can also be used even when individual participant data are not available and we have only summary contingency table data. One benefit of this one-stage regression model over conventional meta-analysis methods is that it maximises the correct binomial likelihood for the data and so does not require the common assumption that effect estimates are normally distributed. A second benefit of using this model is that it may be applied, with only minor modification, in a range of meta-analytic scenarios, including meta-regression, network meta-analyses and meta-analyses of diagnostic test accuracy. This single model can potentially replace the variety of often complex methods used in these areas. This paper considers, with a range of meta-analysis examples, how random-effects logistic regression models may be used in a number of different types of meta-analyses. This one-stage approach is compared with widely used meta-analysis methods including Bayesian network meta-analysis and the bivariate and hierarchical summary receiver operating characteristic (ROC) models for meta-analyses of diagnostic test accuracy.

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Kim, Ye-Jung, and Ji-Young Lim. "Predictive Models for Nurses’ Entrepreneurial Intentions Using Comparison of Competing Models." International Journal of Environmental Research and Public Health 19, no. 10 (May 16, 2022): 6027. http://dx.doi.org/10.3390/ijerph19106027.

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There is a need to introduce predictive models of nurses’ entrepreneurial intentions that can identify which variables will promote entrepreneurship among nurses. This study aimed to explore the factors influencing nurses’ entrepreneurial intentions. We performed a systematic review and developed prediction models using factors identified and validated in a meta-analysis. Moreover, we individually tested and compared three models based on: (1) the Theory of Planned Behavior, (2) a meta-analysis, and (3) a combination of the two. Data from 386 nurses were analyzed using SPSS 23.0 for Windows and AMOS 21.0. The squared multiple correlation statistics of Models 1, 2, and 3 were 54.3%, 35.8%, and 60.0%, respectively. Model 3 provided a better explanation of nurses’ entrepreneurial intentions. Attitude, subjective norm, perceived behavioral control, entrepreneurial orientation, and need for entrepreneurship education are the most important variables to strengthen the entrepreneurial intention of nurses. The results of this study can be used as a theoretical model to explain nurse entrepreneurship intentions. In addition, these findings offer a useful resource for constructing a start-up curriculum within nursing colleges that fosters prospective nursing entrepreneurs.

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Siau, Keng, and Yuan Long. "Synthesizing e‐government stage models – a meta‐synthesis based on meta‐ethnography approach." Industrial Management & Data Systems 105, no. 4 (May 2005): 443–58. http://dx.doi.org/10.1108/02635570510592352.

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Nikolakopoulou, Adriani, Dimitris Mavridis, and Georgia Salanti. "How to interpret meta-analysis models: fixed effect and random effects meta-analyses." Evidence Based Mental Health 17, no. 2 (April 23, 2014): 64. http://dx.doi.org/10.1136/eb-2014-101794.

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Ruiz, Diana Madrigal, M. Tim Tinker, Bernie R. Tershy, Kelly M. Zilliacus, and Donald A. Croll. "Using meta-population models to guide conservation action." Global Ecology and Conservation 28 (August 2021): e01644. http://dx.doi.org/10.1016/j.gecco.2021.e01644.

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Kelley, George A. "Statistical models for meta-analysis: A brief tutorial." World Journal of Methodology 2, no. 4 (2012): 27. http://dx.doi.org/10.5662/wjm.v2.i4.27.

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Sawyer, Alan G., John U. Farley, and Donald R. Lehmann. "Meta-Analysis in Marketing: Generalization of Response Models." Journal of Marketing Research 24, no. 4 (November 1987): 451. http://dx.doi.org/10.2307/3151395.

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Siau, Keng, Fiona F. H. Nah, and Qing Cao. "A Meta-Analysis Comparing Relational and Semantic Models." Journal of Database Management 22, no. 4 (October 2011): 57–72. http://dx.doi.org/10.4018/jdm.2011100103.

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Data modeling is the sine quo non of systems development and one of the most widely researched topics in the database literature. In the past three decades, semantic data modeling has emerged as an alternative to traditional relational modeling. The majority of the research in data modeling suggests that the use of semantic data models leads to better performance; however, the findings are not conclusive and are sometimes inconsistent. The discrepancies that exist in the data modeling literature and the relatively low statistical power in the studies make meta-analysis a viable choice in analyzing and integrating the findings of these studies.

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AL-Alimi, Dalal, Mohammed A. A. Al-qaness, Zhihua Cai, Abdelghani Dahou, Yuxiang Shao, and Sakinatu Issaka. "Meta-Learner Hybrid Models to Classify Hyperspectral Images." Remote Sensing 14, no. 4 (February 21, 2022): 1038. http://dx.doi.org/10.3390/rs14041038.

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Hyperspectral (HS) images are adjacent band images that are generally used in remote-sensing applications. They have numerous spatial and spectral information bands that are extremely useful for material detection in various fields. However, their high dimensionality is a big challenge that affects their overall performance. A new data normalization method was developed to enhance the variations and data distribution using the output of principal component analysis (PCA) and quantile transformation, called QPCA. This paper also proposes a novel HS images classification framework using the meta-learner technique to train multi-class and multi-size datasets by concatenating and training the hybrid and multi-size kernel of convolutional neural networks (CNN). The high-level model works to combine the output of the lower-level models and train them with the new input data, called meta-learner hybrid models (MLHM). The proposed MLHM framework with our external normalization (QPCA) improves the accuracy and outperforms other approaches using three well-known benchmark datasets. Moreover, the evaluation outcomes showed that the QPCA enhanced the framework accuracy by 13% for most models and datasets and others by more than 25%, and MLHM provided the best performance.

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Khanra, Sayantan, and Rojers P. Joseph. "E-Governance Maturity Models: A Meta-ethnographic Study." International Technology Management Review 8, no. 1 (2019): 1. http://dx.doi.org/10.2991/itmr.b.190417.001.

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Chung, Younshik, Dipak Dey, and Junghoon Jang. "Semiparametric hierarchical selection models for bayesian meta analysis." Journal of Statistical Computation and Simulation 72, no. 10 (January 2002): 825–39. http://dx.doi.org/10.1080/00949650214672.

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Sultan, Fareena, John U. Farley, and Donald R. Lehmann. "A Meta-Analysis of Applications of Diffusion Models." Journal of Marketing Research 27, no. 1 (February 1990): 70. http://dx.doi.org/10.2307/3172552.

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Iacoboni, Marco. "Hybrid Social Cognitive Models, Meta-Consciousness, and representations." Cognitive Neuroscience 2, no. 2 (June 2011): 118–19. http://dx.doi.org/10.1080/17588928.2011.585236.

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Hedges, Larry V., and Jack L. Vevea. "Fixed- and random-effects models in meta-analysis." Psychological Methods 3, no. 4 (1998): 486–504. http://dx.doi.org/10.1037/1082-989x.3.4.486.

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DeChurch, Leslie A., and Jessica R. Mesmer-Magnus. "Measuring shared team mental models: A meta-analysis." Group Dynamics: Theory, Research, and Practice 14, no. 1 (2010): 1–14. http://dx.doi.org/10.1037/a0017455.

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Sultan, Fareena, John U. Farley, and Donald R. Lehmann. "A Meta-Analysis of Applications of Diffusion Models." Journal of Marketing Research 27, no. 1 (February 1990): 70–77. http://dx.doi.org/10.1177/002224379002700107.

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Athron, Peter, Jae-hyeon Park, Dominik Stöckinger, and Alexander Voigt. "FlexibleSUSY – a meta spectrum generator for supersymmetric models." Nuclear and Particle Physics Proceedings 273-275 (April 2016): 2424–26. http://dx.doi.org/10.1016/j.nuclphysbps.2015.09.413.

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Caiuta, Rafael, Aurora Pozo, and Silvia Regina Vergilio. "Meta-learning based selection of software reliability models." Automated Software Engineering 24, no. 3 (March 21, 2016): 575–602. http://dx.doi.org/10.1007/s10515-016-0195-9.

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Bottomley, Paul. "A meta-analysis of applications of diffusion models." International Journal of Forecasting 6, no. 4 (December 1990): 584–85. http://dx.doi.org/10.1016/0169-2070(90)90047-f.

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Journal articles on the topic 'Meta Models'

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