Log in

Relevant bibliographies by topics / Mixed model

Contents

Journal articles
Dissertations / Theses
Books
Book chapters
Conference papers
Reports

Academic literature on the topic 'Mixed model'

Author: Grafiati

Published: 4 June 2021

Last updated: 20 April 2024

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Mixed model.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

More sources

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

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

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

3

Overstall, Antony Marshall. "Default Bayesian model determination for generalised linear mixed models." Thesis, University of Southampton, 2010. https://eprints.soton.ac.uk/170229/.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

4

Sima, Adam. "Accounting for Model Uncertainty in Linear Mixed-Effects Models." VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/2950.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

5

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

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

Full text

Abstract:

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 multi-sample sphericity (MMS), regarding the equality and structure of orthonormalized population covariance matrices is satisfied (given multivariate normality and independence for distributions of subjects' scores), valid likelihood-ratio MMM tests of group-by-occasion interaction and occasions hypotheses are possible. To date, no information has been available concerning actual (empirical) levels of significance of such tests when the MMS condition is violated. This study was conducted to begin to provide such information. Departure from the MMS condition can be classified into three types— termed departures of types A, B, and C respectively: (A) the covariance matrix for population ℊ (ℊ = 1,...G), when orthonormalized, has an equal-diagonal-block form but the resulting matrix for population ℊ is unequal to the resulting matrix for population ℊ' (ℊ ≠ ℊ'); (B) the G populations' orthonormalized covariance matrices are equal, but the matrix common to the populations does not have equal-diagonal-block structure; or (C) one or more populations has an orthonormalized covariance matrix which does not have equal-diagonal-block structure and two or more populations have unequal orthonormalized matrices. In this study, Monte Carlo procedures were used to examine the effect of each type of violation in turn on the Type I error rates of multivariate mixed model tests of group-by-occasion interaction and occasions null hypotheses. For each form of violation, experiments modelling several levels of severity were simulated. In these experiments: (a) the number of measured variables was two; (b) the number of measurement occasions was three; (c) the number of populations sampled was two or three; (d) the ratio of average sample size to number of measured variables was six or 12; and (e) the sample size ratios were 1:1 and 1:2 when G was two, and 1:1:1 and 1:1:2 when G was three. In experiments modelling violations of types A and C, the effects of negative and positive sampling were studied. When type A violations were modelled and samples were equal in size, actual Type I error rates did not differ significantly from nominal levels for tests of either hypothesis except under the most severe level of violation. In type A experiments using unequal groups in which the largest sample was drawn from the population whose orthogonalized covariance matrix has the smallest determinant (negative sampling), actual Type I error rates were significantly higher than nominal rates for tests of both hypotheses and for all levels of violation. In contrast, empirical levels of significance were significantly lower than nominal rates in type A experiments in which the largest sample was drawn from the population whose orthonormalized covariance matrix had the largest determinant (positive sampling). Tests of both hypotheses tended to be liberal in experiments which modelled type B violations. No strong relationships were observed between actual Type I error rates and any of: severity of violation, number of groups, ratio of average sample size to number of variables, and relative sizes of samples. In equal-groups experiments modelling type C violations in which the orthonormalized pooled covariance matrix departed at the more severe level from equal-diagonal-block form, actual Type I error rates for tests of both hypotheses tended to be liberal. Findings were more complex under the less severe level of structural departure. Empirical significance levels did not vary with the degree of interpopulation heterogeneity of orthonormalized covariance matrices. In type C experiments modelling negative sampling, tests of both hypotheses tended to be liberal. Degree of structural departure did not appear to influence actual Type I error rates but degree of interpopulation heterogeneity did. Actual Type I error rates in type C experiments modelling positive sampling were apparently related to the number of groups. When two populations were sampled, both tests tended to be conservative, while for three groups, the results were more complex. In general, under all types of violation the ratio of average group size to number of variables did not greatly affect actual Type I error rates. The report concludes with suggestions for practitioners considering use of the MMM procedure based upon the findings and recommends four avenues for future research on Type I error robustness of MMM analysis of variance. The matrix pool and computer programs used in the simulations are included in appendices.
Education, Faculty of
Educational and Counselling Psychology, and Special Education (ECPS), Department of
Graduate

APA, Harvard, Vancouver, ISO, and other styles

7

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

Full text

Abstract:

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 have been proposed to obtain them. We consider the subsampling algorithm to obtain the MVE estimators and the FAST-MCD algorithm to obtain the MCD estimators. Previous studies have not clearly determined which of these two estimation methods is best for control chart applications. The comprehensive simulation study here gives guidance for when to use which estimator. Control limits are provided. High breakdown estimation methods such as MCD and MVE can be applied to a wide variety of multivariate quality control data. The final, lengthier portion of this research considers profile monitoring. Profile monitoring is a relatively new technique in quality control used when the product or process quality is best represented by a profile (or a curve) at each time period. The essential idea is often to model the profile via some parametric method and then monitor the estimated parameters over time to determine if there have been changes in the profiles. Because the estimated parameters may be correlated, it is convenient to monitor them using a multivariate control method such as the T-squared statistic. Previous modeling methods have not incorporated the correlation structure within the profiles. We propose the use of mixed models (both linear and nonlinear) to monitor linear and nonlinear profiles in order to account for the correlation structure within a profile. We consider various data scenarios and show using simulation when the mixed model approach is preferable to an approach that ignores the correlation structure. Our focus is on Phase I control chart applications.
Ph. D.

APA, Harvard, Vancouver, ISO, and other styles

8

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

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.

Full text

Abstract:

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 problems with minimal interruption of the real traffic environment and minimum costs. Compared within previously studied models, the mixed (SPM and GQM) mod- els, especially using the gamma or lognormal distributions to describe followers headways, are probably the most recognized ones by researchers in statistical stud- ies of headway data. These mixed models are reported with good fitting results indicated by goodness-of-fit tests, and some of them are better than others in com- putational costs. The gamma-SPM and gamma-GQM models are often reported to have similar fitting qualities, and they often out-perform the lognormal-GQM model in terms of computational costs. A lognormal-SPM model cannot be formed analytically as no explicit Laplace transform is available with the lognormal dis- tribution. The major downsides of using mixed models are the difficulties and more flexibilities in fitting process as they have more parameters than those single models, and this sometimes leads to unsuccessful fitting or unreasonable fitted pa- rameters despite their success in passing GoF tests. Furthermore, it is difficult to know the connections between model parameters and realistic traffic situations or environments, and these parameters have to be estimated using headway samples. Hence, it is almost impossible to explain any traffic phenomena with the param- eters of a model. Moreover, with the gamma distribution as the only common well-known followers headway model, it is hard to justify whether it has described the headway process appropriately. This creates a barrier for better understanding the process of how drivers would follow their preceding vehicles. This study firstly proposes a framework developed using MATLAB, which would help researchers in quick implementations of any headway distributions of interest. This framework uses common methods to manage and prepare headway samples to meet those requirements in data analysis. It also provides common structures and methods on implementing existing or new models, fitting models, testing their performance hence reporting results. This will simplify the development work involved in headway analysis, avoid unnecessary repetitions of work done by others and provide results in formats that are more comparable with those reported by others. Secondly, this study focuses on the implementation of existing mixed models, i.e. the gamma-SPM, gamma-GQM and lognormal-GQM, using the proposed framework. The lognormal-SPM is also tested for the first time, with the recently developed approximation method of Laplace transform available for lognormal distributions. The parameters of these mixed models are specially discussed, as means of restrictions to simplify the fitting process of these models. Three ways of parameter pre-determinations are attempted over gamma-SPM and gamma-GQM models. A couple of response-time (RT) distributions are focused on in the later part of this study. Two RT models, i.e. Ex-Gaussian (EMG) and inverse Gaussian (IVG) are used, for first time, as single models to describe headway data. The fitting performances are greatly comparable to the best known lognormal single model. Further extending this work, these two models are tested as followers headway distributions in both SPM and GQM mixed models. The test results have shown excellent fitting performance. These now bring researchers more alternatives to use mixed models in headway analysis, and this will help to compare the be- haviours of different models when they are used to describe followers headway data. Again, similar parameter restrictions are attempted for these new mixed models, and the results show well-acceptable performance, and also corrections on some unreasonable fittings caused by the over flexibilities using 4- or 5- parameter models.

APA, Harvard, Vancouver, ISO, and other styles

More sources

Books on the topic "Mixed model"

1

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

Find full text

APA, Harvard, Vancouver, ISO, and other styles

2

Selection index and introduction to mixed model methods. Boca Raton: CRC Press, 1993.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

3

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

Find full text

APA, Harvard, Vancouver, ISO, and other styles

4

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

Find full text

APA, Harvard, Vancouver, ISO, and other styles

5

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

Find full text

APA, Harvard, Vancouver, ISO, and other styles

6

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

Find full text

APA, Harvard, Vancouver, ISO, and other styles

7

Extending the linear model with R: Generalized linear, mixed effects and nonparametric regression models. Boca Raton: Taylor & Francis, 2016.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

8

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

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

Find full text

APA, Harvard, Vancouver, ISO, and other styles

10

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

Find full text

APA, Harvard, Vancouver, ISO, and other styles

More sources

Book chapters on the topic "Mixed model"

1

Takezawa, Kunio. "Linear Mixed Model." In Learning Regression Analysis by Simulation, 269–94. Tokyo: Springer Japan, 2013. http://dx.doi.org/10.1007/978-4-431-54321-3_6.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Buber, Renate, Bernhart Ruso, and Johannes Gadner. "Mixed-Model-Design." In Qualitative Marktforschung, 883–901. Wiesbaden: Gabler, 2009. http://dx.doi.org/10.1007/978-3-8349-9441-7_53.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Plant, Richard E. "The Mixed Model." In Spatial Data Analysis in Ecology and Agriculture Using R, 413–44. Second edition. | Boca Raton, Florida : CRC Press, [2019]: CRC Press, 2018. http://dx.doi.org/10.1201/9781351189910-12.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Malik, Jamil A., Theresa A. Morgan, Falk Kiefer, Mustafa Al’Absi, Anna C. Phillips, Patricia Cristine Heyn, Katherine S. Hall, et al. "Linear Mixed-Effects Model." In Encyclopedia of Behavioral Medicine, 1163. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1005-9_100981.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Scholl, Armin. "Mixed-Model Assembly Lines." In Balancing and Sequencing of Assembly Lines, 75–110. Heidelberg: Physica-Verlag HD, 1995. http://dx.doi.org/10.1007/978-3-662-00861-4_3.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Gałecki, Andrzej, and Tomasz Burzykowski. "Linear Mixed-Effects Model." In Springer Texts in Statistics, 245–73. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3900-4_13.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Holland, Paul M. "Nonideal Mixed Monolayer Model." In ACS Symposium Series, 102–15. Washington, DC: American Chemical Society, 1986. http://dx.doi.org/10.1021/bk-1986-0311.ch008.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Edda, Weig. "The Mixed Game Model." In The Routledge Handbook of Language and Dialogue, 174–94. New York, NY : Routledge, [2017] | Series: Routledge Handbooks in Linguistics: Routledge, 2017. http://dx.doi.org/10.4324/9781315750583-12.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Scholl, Armin. "Mixed-Model Assembly Lines." In Balancing and Sequencing of Assembly Lines, 77–112. Heidelberg: Physica-Verlag HD, 1999. http://dx.doi.org/10.1007/978-3-662-11223-6_3.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Kouvaritakis, Basil, and Mark Cannon. "Robust MPC for Multiplicative and Mixed Uncertainty." In Model Predictive Control, 175–240. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24853-0_5.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Mixed model"

1

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Barner, Simon, Alexander Diewald, Jorn Migge, Ali Syed, Gerhard Fohler, Madeleine Faugere, and Daniel Gracia Perez. "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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Zamani, Mohsen, Elisabeth Felsenstein, Brian D. O. Anderson, and Manfred Deistler. "Mixed frequency structured AR model identification." In 2013 European Control Conference (ECC). IEEE, 2013. http://dx.doi.org/10.23919/ecc.2013.6669430.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Mixed model"

1

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

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), July 2002. http://dx.doi.org/10.2172/799235.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

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), July 2016. http://dx.doi.org/10.2172/1592703.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

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), November 1989. http://dx.doi.org/10.2172/5087936.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

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), November 2011. http://dx.doi.org/10.2172/1035597.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!