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1
Ribbing, Jakob. "Covariate Model Building in Nonlinear Mixed Effects Models." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-7923.
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Waterman, Megan Janet Tuttle. "Linear Mixed Model Robust Regression." Diss., Virginia Tech, 2002. http://hdl.handle.net/10919/27708.
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Mixed models are powerful tools for the analysis of clustered data and many extensions of the classical linear mixed model with normally distributed response have been established. As with all parametric models, correctness of the assumed model is critical for the validity of the ensuing inference. Model robust regression techniques predict mean response as a convex combination of a parametric and a nonparametric model fit to the data. It is a semiparametric method by which incompletely or incorrectly specified parametric models can be improved through adding an appropriate amount of a 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.
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Overstall, Antony Marshall. "Default Bayesian model determination for generalised linear mixed models." Thesis, University of Southampton, 2010. https://eprints.soton.ac.uk/170229/.
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In this thesis, an automatic, default, fully Bayesian model determination strategy for GLMMs is considered. This strategy must address the two key issues of default prior specification and computation. Default prior distributions for the model parameters, that are based on a unit information concept, are proposed. A two-phase computational strategy, that uses a reversible jump algorithm and implementation of bridge sampling, is also proposed. This strategy is applied to four examples throughout this thesis.
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Sima, Adam. "Accounting for Model Uncertainty in Linear Mixed-Effects Models." VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/2950.
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Standard statistical decision-making tools, such as inference, confidence intervals and forecasting, are contingent on the assumption that the statistical model used in the analysis is the true model. In linear mixed-effect models, ignoring model uncertainty results in an underestimation of the residual variance, contributing to hypothesis tests that demonstrate larger than nominal Type-I errors and confidence intervals with smaller than nominal coverage probabilities. A novel utilization of the generalized degrees of freedom developed by Zhang et al. (2012) is used to adjust the estimate of 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.
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Li, Qie. "A Bayesian Hierarchical Model for Multiple Comparisons in Mixed Models." Bowling Green State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1342530994.
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Prosser, Robert James. "Robustness of multivariate mixed model ANOVA." Thesis, University of British Columbia, 1985. http://hdl.handle.net/2429/25511.
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In experimental or quasi-experimental studies in which a repeated measures design is used, it is common to obtain scores on several dependent variables on each measurement occasion. Multivariate mixed model (MMM) analysis of variance (Thomas, 1983) is a recently developed alternative to the MANOVA procedure (Bock, 1975; Timm, 1980) for testing multivariate hypotheses concerning effects of a repeated factor (called occasions in this study) and interaction between repeated and non-repeated factors (termed group-by-occasion interaction here). If a condition derived by Thomas (1983), multivariate 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
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Jensen, Willis Aaron. "Profile Monitoring for Mixed Model Data." Diss., Virginia Tech, 2006. http://hdl.handle.net/10919/27054.
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The initial portion of this research focuses on appropriate parameter estimators within a general context of multivariate quality control. The goal of Phase I analysis of multivariate quality control data is to identify multivariate outliers and step changes so that the estimated control limits are sufficiently accurate for Phase II monitoring. High breakdown estimation methods based on the minimum volume ellipsoid (MVE) or the minimum covariance determinant (MCD) are well suited to detecting multivariate outliers in data. Because of the inherent difficulties in computation many algorithms 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.
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Wenren, Cheng. "Mixed Model Selection Based on the Conceptual Predictive Statistic." Bowling Green State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1403735738.
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Pan, Juming. "Adaptive LASSO For Mixed Model Selection via Profile Log-Likelihood." Bowling Green State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1466633921.
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Yu, Fu. "On statistical analysis of vehicle time-headways using mixed distribution models." Thesis, University of Dundee, 2014. https://discovery.dundee.ac.uk/en/studentTheses/d101df63-b7db-45b6-8a03-365b64345e6b.
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For decades, vehicle time-headway distribution models have been studied by many researchers and traffic engineers. A good time-headway model can be beneficial to traffic studies and management in many aspects; e.g. with a better understanding of road traffic patterns and road user behaviour, the researchers or engineers can give better estimations and predictions under certain road traffic conditions and hence make better decisions on traffic management and control. The models also help us to implement high-quality microscopic traffic simulation studies to seek good solutions to traffic 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.
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Ucar, Emre. "Mixed-model Two-sided Assembly Line Balancing." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/3/12611688/index.pdf.
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In this study we focus on two-sided mixed-model assembly line balancing type-I
problem. There is a production target for a fixed time horizon and the objective is
to produce this amount with the minimum level of workforce. A mathematical
model is developed to solve this problem in an optimal manner. For large scale
problems, the mathematical model fails to give the optimal solution within
reasonable computational times. Thus, a heuristic approach based on threshold
accepting algorithm is presented. Both the mathematical model and the heuristic
approach are executed to solve several example problems from the literature and
a case study problem which is derived from the refrigerator production.
Computational experiments are carried out using both approaches. It is observed
that the heuristic procedure finds good solutions within very reasonable
computational times.
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Cronje, Tanita. "A mixed model approach to conjoint analysis." Diss., University of Pretoria, 2014. http://hdl.handle.net/2263/79776.
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In this decision-driven era, it has become vital for modelers to efficiently model consumer
choices and preferences (from a marketing perspective for instance). Conjoint analysis
is a known method which has been used to perform such analyses. A mixed effects
model is proposed to perform a conjoint analysis with normal responses, illustrated by
an application of modeling respondent’s preferences to different industrial detergents.
The proposed model allows for predicting how observed attributes (which describes a
product in terms of its characteristics and features) of decision makers and choice options,
influence decisions. Inference regarding the parameters of the proposed model with a
normal distribution is discussed in the mixed effect conjoint setting. Extensions of this
model, regarding Bayesian prior selection are also discussed.Dissertation (MSc)--University of Pretoria, 2014.
Statistics
MSc
Unrestricted
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Alabiso, Audry. "Linear Mixed Model Selection by Partial Correlation." Bowling Green State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1587142724497829.
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Dixon, Cheryl Annette. "Power Analysis for the Mixed Linear Model." VCU Scholars Compass, 1996. http://scholarscompass.vcu.edu/etd/4525.
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Power analysis is becoming standard in inference based research proposals and is used to support the proposed design and sample size. The choice of an appropriate power analysis depends on the choice of the research question, measurement procedures, design, and analysis plan. The "best" power analysis, however, will have many features of a sound data analysis. First, it addresses the study hypothesis, and second, it yields a credible answer.
Power calculations for standard statistical hypotheses based on normal theory have been defined for t-tests through the univariate and multivariate general linear models. For these statistical methods, the approaches to power calculations have been presented based on the exact or approximate distributions of the test statistics in question. Through the methods proposed by O'Brien and Muller (1993), the noncentrality parameter for the noncentral distribution of the test statistics for the univariate and multivariate general linear models is expressed in terms of its distinct components. This in tum leads to methods for calculating power which are efficient and easy to implement.
As more complex research questions are studied, more involved methods have been proposed to analyze data. One such method includes the mixed linear model. This research extends the approach to power calculation used for the general linear model to the mixed linear model. Power calculations for the mixed linear model will be based on the approximate F statistic for testing the mixed model's fixed effects proposed by Helms (1992). The noncentrality parameter of the approximate noncentral F for the mixed model will be written in terms of its distinct components so that a useful and efficient method for calculating power in the mixed model setting will be achieved. In this research, it has been found that the rewriting of the noncentrality parameter varies depending on study design. Thus, the noncentrality parameter for three specific cases of study design are derived.
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Schwardt, Ludwig. "Efficient Mixed-Order Hidden Markov Model Inference." Thesis, Link to the online version, 2007. http://hdl.handle.net/10019/709.
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Abraham, Anita Ann Edwards Lloyd J. "Model selection methods in the linear mixed model for longitudinal data." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2008. http://dc.lib.unc.edu/u?/etd,1859.
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Thesis (DrPH)--University of North Carolina at Chapel Hill, 2008.Title from electronic title page (viewed Dec. 11, 2008). "... in partial fulfillment of the requirements for the degree of Doctor of Public Health in the Department of Biostatistics, School of Public Health." Discipline: Biostatistics; Department/School: Public Health.
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Gory, Jeffrey J. "Marginally Interpretable Generalized Linear Mixed Models." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1497966698387606.
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Yousef, Mohammed A. "Two-Stage SCAD Lasso for Linear Mixed Model Selection." Bowling Green State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1558431514460879.
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Yam, Ho-kwan, and 任浩君. "On a topic of generalized linear mixed models and stochastic volatility model." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2002. http://hub.hku.hk/bib/B29913342.
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Kneib, Thomas. "Mixed model based inference in structured additive regression." [S.l.] : [s.n.], 2005. http://edoc.ub.uni-muenchen.de/archive/00005011.
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Kneib, Thomas. "Mixed model based inference in structured additive regression." Diss., lmu, 2006. http://nbn-resolving.de/urn:nbn:de:bvb:19-50112.
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Zhu, Xiaohong. "Numerical methods for a mixed Stokes-Darcy model /." View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?MATH%202007%20ZHU.
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Beckler, Robert Kendall. "Polynuclear metal complexes as model mixed oxide catalysts." Diss., Georgia Institute of Technology, 1987. http://hdl.handle.net/1853/11897.
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Tang, On-yee, and 鄧安怡. "Estimation for generalized linear mixed model via multipleimputations." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B30687652.
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Welham, Suzanne Jane. "Smoothing spline methods within the mixed model framework." Thesis, London School of Hygiene and Tropical Medicine (University of London), 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.425673.
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Lindkvist, Oskar. "Model Adaptation of a Mixed Flow Turbofan Engine." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-80667.
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Gas turbine performance models are usually created in an object oriented manner, where different standard components are connected to form the complete model. The characteristics of these components are often represented by component maps and empirical correlations. However, engine specific component characteristics are seldom available to anyone outside of the manufacturers. It is therefore very common for researchers to use publicly accessible or generic component maps instead. But in order to reduce prediction errors the maps have to be modified to fit any specific engine. This thesis work investigates the process of adapting a parametric turbofan engine model to a limited amount of test-data using the propulsion program EVA. Steady state test-data was generated using an initial reference model with SLS operating conditions. Another engine model with different fan, compressor and turbine maps was then used in the adaptation. An initial on-design model was adapted to the highest power test-data point. This model is based on aerothermodynamic equations and is used as a reference to scale the generic component maps to. A sensitivity analysis was done at this point in order to find dependencies between unknown component parameters and test data. These were then included in the cycle solver which employs a version of the Newton-Raphson method. After the fan and compressor maps had been scaled to the design point they were adapted to test-data by adjusting the mass flow parameters in a direct search optimizer. Finally, speed lines in the fan and compressor maps were relabeled to reduce rotor speed errors. The adapted performance model was then validated against the reference model at a few flying conditions. The performance model results demonstrate that it is possible to greatly reduce prediction errors by only adjusting the corrected mass flow in fan and compressor maps. Additionally, rotor speed errors could successfully be corrected as a final step in the adaptation by relabeling speed lines in the component maps. When validated, the adapted model had a maximum parameter error of 1.5%.
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Cheldelin, Brent. "Design for mixed model production of complex products /." May be available electronically:, 2007. http://proquest.umi.com/login?COPT=REJTPTU1MTUmSU5UPTAmVkVSPTI=&clientId=12498.
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Wang, Mengyu. "Model-based Optimisation of Mixed Refrigerant LNG Processes." Thesis, The University of Sydney, 2017. http://hdl.handle.net/2123/17387.
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Natural gas liquefaction processes are energy and cost intensive. This thesis pursues the optimisation of propane precooled mixed refrigerant (C3MR) processes considering variations in upstream gas well conditions, in order to maximise gas well life. Four objective functions were selected for the design optimisation of the C3MR and dual mixed refrigerant (DMR) processes: 1) total shaft work (W), 2) total capital investment, 3) total annualised cost, and 4) total capital cost of both compressors and main cryogenic heat exchanger (MCHE). Optimisation results show that objective function 4 is more suitable than other objective functions for reducing both W and UA (MCHE design parameter). This leads to 15% reduction in specific power for C3MR and 27% for DMR, while achieving lower UA values relative to baseline. The operation optimisation of the C3MR process and its split propane version (C3MR-SP) was performed using four objective functions: 1) total shaft work, 2-3) two different exergy efficiency expressions, and 4) operating expenditure (OPEX). Objective function 3 results in the lowest specific shaft work 1469 MJ/tonne-LNG. For C3MR-SP, however, the lowest specific shaft work is found to be under objective function 1. A comparison of optimisation results across literature studies is impractical due to dissimilar process conditions, feed gas conditions, product quality, and equipment size. A sensitivity analysis highlights the effect of feed gas conditions on performance of the C3MR. For instance, as LNG production decreases from 3 MTPA to 2.4 MTPA over time, the specific OPEX increases from $128/tonne-LNG to $154/tonne-LNG. A subsequent study was conducted focusing on energy benefits of two configurations: integrating natural gas liquids (NGL) recovery unit with C3MR. An integrated NGL recovery within C3MR shows a 0.74% increase in energy consumption as methane concentration of the feed gas decreases, however a frontend NGL recovery unit only has a 0.18% decrease.
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Jesser, Alexander. "Mixed signal circuit verification using symbolic model checking techniques." München Verl. Dr. Hut, 2008. http://d-nb.info/992162858/04.
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Lee, Yi-Ching. "An Approach to Estimation and Selection in Linear Mixed Models with Missing Data." Bowling Green State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1562754262770979.
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Wu, Hongqian. "Proportional likelihood ratio mixed model for longitudinal discrete data." Diss., University of Iowa, 2016. https://ir.uiowa.edu/etd/2296.
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A semiparametric proportional likelihood ratio model was proposed by Luo and Tsai (2012) which is suitable for modeling a nonlinear monotonic relationship between the response variable and a covariate. Extending the generalized linear model, this model leaves the probability distribution unspecified but estimates it from the data. In this thesis, we propose to extend this model into analyzing the longitudinal data by incorporating random effects into the linear predictor. By using this model as the conditional density of the response variable given the random effects, we present a maximum likelihood approach for model estimation and inference. Two numerical estimation procedures were developed for response variables with finite support, one based on the Newton-Raphson algorithm and the other one based on generalized expectation maximization (GEM) algorithm. In both estimation procedures, Gauss-Hermite quadrature is employed to approximate the integrals.
Upon convergence, the observed information matrix is estimated through the second-order numerical differentiation of the log likelihood function. Asymptotic properties of the maximum likelihood estimator are established under certain regularity conditions and simulation studies are conducted to assess its finite sample properties and compare the proposed model to the generalized linear mixed model. The proposed method is illustrated in an analysis of data from a multi-site observational study of prodromal Huntington's disease.
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Wang, Jun. "Selecting the Best Linear Mixed Model Using Predictive Approaches." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd1697.pdf.
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Burdett, Robert. "Sequencing and scheduling theory for mixed-model multi-stage assembly environment." Thesis, Queensland University of Technology, 2001.
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Tang, On-yee. "Estimation for generalized linear mixed model via multiple imputations." Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B30687652.
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Holth, Erik. "Model Predictive Control of mixed solar and electric heating." Thesis, Norwegian University of Science and Technology, Department of Engineering Cybernetics, 2009. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9106.
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In this report we will model a heat system consisting of a heat storage tank and an application. The heat storage tank is supplied by a heating element and heated water from a solar collector. The main objective of the heat system is to mainatian a reference temperature in the application (a house). Weather forecasts will be used as weather data affecting the heat system. We will assume that the weather forecasts and the actual weather will be the same. The heat sytem will consist of simplified nonlinear differential equations and be controlled by a model predictive controller (mpc). The mpc controller will use a linearized model of the nonlinear process. The average predicted outside temperature from the weather forecasts will be used as nominal value for the same temperature in the linearized model in the mpc controller. The mpc controller will measure some disturbances to make more efficient control. The most imortant disturbance will be the temperature of the water coming out of the solar collector, that will flow into the heat storage. By measuring this temperature, the mpc controller can apply it to its predictor and make sure that the power of the heating element in the heat storage is reduced when solar collector heated water is available. This is to make sure that the heat storage has enough capacity to receive the heated water from the solar collector, while still maintaining a reasonable temperature in the heat storage. Simulation with different weighting of the inputs in the mpc controller will show that heating element power consumption is influenced by these weights.
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Kliegl, Reinhold, Michael E. J. Masson, and Eike M. Richter. "A linear mixed model analysis of masked repetition priming." Universität Potsdam, 2009. http://opus.kobv.de/ubp/volltexte/2011/5707/.
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We examined individual differences in masked repetition priming by re-analyzing item-level response-time (RT) data from three experiments. Using a linear mixed model (LMM) with subjects and items specified as crossed random factors, the originally reported priming and word-frequency effects were recovered. In the
same LMM, we estimated parameters describing the distributions of these effects across subjects. Subjects’ frequency and priming effects correlated positively with each other and negatively with mean RT. These correlation estimates, however, emerged only with a reciprocal transformation of RT (i.e., -1/RT), justified on the basis of distributional analyses. Different correlations, some with opposite sign, were obtained (1) for untransformed or logarithmic RTs or (2) when correlations were computed using within-subject analyses. We discuss the relevance of the new results for accounts of masked priming, implications of applying RT transformations, and the use of LMMs as a tool for the joint analysis of experimental effects and associated individual differences.
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Lan, Lan. "Variable Selection in Linear Mixed Model for Longitudinal Data." NCSU, 2006. http://www.lib.ncsu.edu/theses/available/etd-05172006-211924/.
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Fan and Li (JASA, 2001) proposed a family of variable selection procedures for certain parametric models via a nonconcave penalized likelihood approach, where significant variable selection and parameter estimation were done simultaneously, and the procedures were shown to have the oracle property. In this presentation, we extend the nonconcave penalized likelihood approach to linear mixed models for longitudinal data. Two new approaches are proposed to select significant covariates and estimate fixed effect parameters and variance components. In particular, we show the new approaches also possess the oracle property when the tuning parameter is chosen appropriately. We assess the performance of the proposed approaches via simulation and apply the procedures to data from the Multicenter AIDS Cohort Study.
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Hone, David M. "Time and space resolution and mixed layer model accuracy." Monterey, California. Naval Postgraduate School, 1997. http://hdl.handle.net/10945/9080.
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The oceanic turbulent boundary layer is a critical region to understand for oceanic and atmospheric prediction. This thesis answers two fundamental questions: (1) what is the response of the ocean mixed layer system to transient forcing at the air sea surface? (2) what is the necessary time and space resolution in an ocean mixed layer model to resolve important transient responses? Beginning with replication of de Szoeke and Rhines' work, additional physical processes were added to include more realistic viscous dissipation and anisotropy in the three-dimensional turbulent kinetic energy (TKE) budget. These refinements resulted in modification of de Szoeke and Rhines' findings. Firstly, TKE unsteadiness is important for a minimum of 10 to the 5th power seconds. Secondly, viscous dissipation should not be approximated as simply proportional to shear production. Thirdly, entrainment shear production remains significant for a minimum of one pendulum-day. The required temporal model resolution is dependent on the phenomena to be studied. This study focused on the diurnal, synoptic, and annual cycles, which the one-hour time step of the Naval Postgraduate School model adequately resolves. The study of spatial resolution showed unexpectedly that model skill was comparable for 1 m, 10 m and even 20 m vertical grid spacing
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Jacobs, Katrien. "Illumination for mixed reality of complex-to-model scenes." Thesis, University College London (University of London), 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.440451.
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Al-Shaikh, Enas. "Longitudinal Regression Analysis Using Varying Coefficient Mixed Effect Model." University of Cincinnati / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1342543464.
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Atutey, Olivia Abena. "Linear Mixed Model Selection via Minimum Approximated Information Criterion." Bowling Green State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1594910831256966.
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Baumann, Frank Stephan. "Oxygen reduction kinetics on mixed conducting SOFC model cathodes." [S.l. : s.n.], 2006. http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-27056.
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Le, Chenechal Morgan. "Awareness Model for Asymmetric Remote Collaboration in Mixed Reality." Thesis, Rennes, INSA, 2016. http://www.theses.fr/2016ISAR0006/document.
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Etre capable de collaborer à distance avec d'autres personnes peut fournir de précieuses capacités pour effectuer des tâches qui ont besoin de plusieurs utilisateurs pour être accomplies. De plus, les technologies de Réalité Mixte (RM) sont des outils intéressants pour développer de nouveaux types d'applications offrant des interactions et des possibilités de perception plus naturelles comparées aux systèmes classiques. Dans cette thèse, nous proposons d'améliorer la collaboration distante en utilisant ces technologies de RM qui profitent de nos capacités naturelles à effectuer des tâches en environnements 3D. En particulier, nous nous concentrons sur les aspects asymétriques impliqués par ce type de collaboration : les rôles, le point de vue (PdV), les dispositifs et le niveau de virtualité de l'application de RM. Premièrement, nous nous intéressons aux problèmes d'awareness et nous proposons un modèle générique capable de décrire précisément une application de RM collaborative en prenant en compte les potentielles dimensions asymétriques. Afin de traiter toutes ces dimensions, nous séparons notre modèle final en deux niveaux qui distingue espaces réels et virtuels pour chaque utilisateur. Dans ce modèle, chaque utilisateur peut générer différents types d'entrées et recevoir des retours de significations différentes dans le but de maintenir leur propre awareness de l'Environnement Virtuel (EV) partagé. Puis, nous présentons une étude utilisateur exploratoire qui s'intéresse aux conséquences de l'asymétrie des PdVs et aux implications induites par la représentation des utilisateurs sur le niveau d'awareness des autres collaborateurs. Deuxièmement, nous appliquons ces observations dans un contexte de guidage à distance qui implique un guide distant aidant un opérateur à réaliser une tâche de maintenance. Pour ce cas d'usage, nous proposons à l'expert d'utiliser une interface de Réalité Virtuelle (AV) pour aider l'opérateur au travers d'une interface de Réalité Augmentée (RA). Nous contribuons à ce domaine en améliorant les capacités de perception de l'environnement distant par l'expert et en proposant des interactions plus naturelles pour guider l'opérateur au travers d'indications non intrusives et intégrées à son environnement réel. Finalement, nous abordons la tâche de co-manipulation qui est une situation encore plus sensible vis-à-vis de l'awareness en collaboration distante. Cette tâche requiert de viser une synchronisation parfaite entre les collaborateurs pour l'accomplir efficacement. Ainsi, le système doit fournir des retours appropriés pour maintenir un haut niveau d'awareness, spécialement concernant l'activité courante des autres. En particulier, nous proposons une technique de co-manipulation hybride, inspirée de notre cas d'utilisation précédent sur la guidage distant, qui mixe la manipulation d'objet virtuel et du PdV d'un autre utilisateurBeing able to collaborate remotely with other people can provide valuable capabilities in performing tasks that require multiple users to be achieved. Moreover, Mixed Reality (MR) technologies are great tools to develop new kinds of applications with more natural interactions and perception abilities compared to classical desktop setups. In this thesis, we propose to improve remote collaboration using these MR technologies that take advantages of our natural skills to perform tasks in 3D environments. In particular, we focus on asymmetrical aspects involved by these kind of collaboration: roles, point of view (PoV), devices and level of virtuality of the MR application. First, we focus on awareness issues and we propose a generic model able to accurately describe a collaborative MR application taking into account potential asymmetry dimensions. In order to address all these dimensions, we split our final model into two layers that separate real and virtual spaces for each user. In this model, each user can generate different kind of input and receive feedbacks with different meanings in order to maintain their own awareness of the shared Virtual Environment (VE). Then, we conduct an exploratory user study to explore the consequences of asymmetric PoVs and the involvement of users' representation in the level of awareness of others' collaborators. Second, we apply our findings to a remote guiding context that implies a remote guide to help an operator in performing a maintenance task. For this use case, we propose to the expert to use a Virtual Reality (VR) interface in order to help the operator through an Augmented Reality (AR) interface. We contribute to this field by enhancing the expert's perceptual abilities of the remote workspace as well as by providing more natural interactions to guide the operator through not intrusive guiding cues integrated to the real world. Last, we address an even more sensitive situation for awareness in remote collaboration that is virtual co-manipulation. It requires to target a perfect synchronization between collaborators in order to achieve the task efficiently. Thus, the system needs to provide appropriate feedbacks to maintain a high level of awareness, especially about what others are currently doing. In particular, we propose a hybrid co-manipulation technique, inspired from our previous remote guiding use case, that mixes virtual object and other's PoV manipulation in the same time
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Greenaway, Mark Jonathan. "Numerically Stable Approximate Bayesian Methods for Generalized Linear Mixed Models and Linear Model Selection." Thesis, The University of Sydney, 2019. http://hdl.handle.net/2123/20233.
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Approximate Bayesian inference methods offer methodology for fitting Bayesian models as fast alternatives to Markov Chain Monte Carlo methods that sometimes have only a slight loss of accuracy. In this thesis, we consider variable selection for linear models, and zero inflated mixed models. Variable selection for linear regression models are ubiquitous in applied statistics. We use the popular g-prior (Zellner, 1986) for model selection of linear models with normal priors where g is a prior hyperparameter. We derive exact expressions for the model selection Bayes Factors in terms of special functions depending on the sample size, number of covariates and R-squared of the model. We show that these expressions are accurate, fast to evaluate, and numerically stable. An R package blma for doing Bayesian linear model averaging using these exact expressions has been released on GitHub. We extend the Particle EM method of (Rockova, 2017) using Particle Variational Approximation and the exact posterior marginal likelihood expressions to derive a computationally efficient algorithm for model selection on data sets with many covariates. Our algorithm performs well relative to existing algorithms, completing in 8 seconds on a model selection problem with a sample size of 600 and 7200 covariates. We consider zero-inflated models that have many applications in areas such as manufacturing and public health, but pose numerical issues when fitting them to data. We apply a variational approximation to zero-inflated Poisson mixed models with Gaussian distributed random effects using a combination of VB and the Gaussian Variational Approximation (GVA). We also incorporate a novel parameterisation of the covariance of the GVA using the Cholesky factor of the precision matrix, similar to Tan and Nott (2018) to resolve associated numerical difficulties.
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Codd, Casey. "A Review and Comparison of Models and Estimation Methods for Multivariate Longitudinal Data of Mixed Scale Type." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1398686513.
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Alves, Victor César Costa [UNESP]. "Painlevé Integrability and mixed P_III-P_V system solutions." Universidade Estadual Paulista (UNESP), 2017. http://hdl.handle.net/11449/149963.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
O presente trabalho trata de um abordagem de aplicações em física dos métodos matemáticos de integrabilidade de Painlevé, por outro lado também aborda o formalismo de hierarquias integráveis e o modelo de 2M-bosons onde são usados métodos de equações diferenciais bem como um método para soluções usando aproximantes de Padé.
The current work aims at applications of mathematical methods of Painlevé integrability in physics, on the other side it also approaches the integrable hierarchies formalism and the 2M-bose model where differential equations methods are used as well as a method for solutions using Padé approximants.
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Casale, Francesco Paolo. "Multivariate linear mixed models for statistical genetics." Thesis, University of Cambridge, 2016. https://www.repository.cam.ac.uk/handle/1810/267465.
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In the last decade, genome-wide association studies have helped to advance our understanding of the genetic architecture of many important traits, including diseases. However, the statistical analysis of genotype-phenotype associations remains challenging due to multiple factors. First, many traits have polygenic architectures, which means that they are controlled by a large number of variants with small individual effects. Second, as increasingly deep phenotype data are being generated there is a need for multivariate analysis approaches to leverage multiple related phenotypes while retaining computational efficiency. Additionally, genetic analyses are confronted by strong confounding factors that can create spurious associations when not properly accounted for in the statistical model. We here derive more flexible methods that allow integrating genetic effects across variants and multiple quantitative traits. To do so, we build on the classical linear mixed model (LMM), a widely adopted framework for genetic studies. The first contribution of this thesis is mtSet, an efficient mixed-model approach that enables genome-wide association testing between sets of genetic variants and multiple traits while accounting for confounding factors. In both simulations and real-data applications we demonstrate that mtSet effectively combines the advantages of variant-set and multi-trait analyses. Next, we present a new model for gene-context interactions that builds on mtSet. The proposed interaction set test (iSet) yields increased statistical power for detecting polygenic interactions. Additionally, iSet enables the identification of genetic loci that are associated with different configurations of causal variants across contexts. After benchmarking the proposed method using simulated data, we consider two applications to real datasets, where we investigate genetic effects on gene expression across different cellular contexts and sex-specific genetic effects on lipid levels. Finally, we describe LIMIX, a software framework for the flexible implementation of different LMMs. Most of the models considered in this thesis, including mtSet and iSet, are implemented and available in LIMIX. A unique aspect of the software is an inference framework that allows a large class of genetic models to be defined and, in many cases, to be efficiently fitted by exploiting specific algebraic properties. We demonstrate the utility of this software suite in two applied collaboration projects. Taken together, this thesis demonstrates the value of flexible and integrative modelling in genetics and contributes new statistical methods for genetic analysis. These approaches generalise previous models, yet retain the computational efficiency that is needed to tackle large genetic datasets.
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Alves, Victor César Costa. "Painlevé Integrability and mixed P_III-P_V system solutions /." São Paulo, 2017. http://hdl.handle.net/11449/149963.
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Orientador: Abraham Hirsz ZimermanAbstract: The current work aims at applications of mathematical methods of Painlevé integrability in physics, on the other side it also approaches the integrable hierarchies formalism and the 2M-bose model where differential equations methods are used as well as a method for solutions using Padé approximants.
Resumo: O presente trabalho trata de um abordagem de aplicações em física dos métodos matemáticos de integrabilidade de Painlevé, por outro lado também aborda o formalismo de hierarquias integráveis e o modelo de 2M-bosons onde são usados métodos de equações diferenciais bem como um método para soluções usando aproximantes de Padé.
Mestre
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Båtelsson, Niklas, and Simon Alfredsson. "Assembly system design - : Case study of a mixed model production." Thesis, KTH, Industriell produktion, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-103276.
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The report, which is a part of the course "MG202X Examensarbete", has been written for the institution Industrial Production at the Royal Institute of Technology (KTH) with guidance from Antonio Maffei. The work has been focused on creating an assembly system at a production facility for Schneider Electric in Nyköping. The Authors has divided the report into a literature review containing Lean production and assembly systems, an analysis of the initial state and a solution. The literature review presents three separate parts which creates the framework of our analysis. The first part regards assembly system and describes different types of design alternatives and which losses that can be found in an assembly system. Furthermore the second part contains Lean production where selected parts of the philosophy are described. The last part of the literature review treats the design of the workstation with regards to ergonomics and part presentation. The analysis at Schneider Electric has been conducted during a three month period and has included time studies, observations and interviews. To analyze the initial state a model for estimating assembly times and workload were needed. An in depth understanding of the initial state was the foundation to be able to create an adapted and accepted assembly system. The work resulted in two suggested assembly systems. One system contains only one workstation and was to be used for a simple assembly process. The second system is to be used for more complex products and has a higher capacity as it contains three workstations. As the assembly system contains three separate workstations it means that the assembly process has been divided which were done through a consideration between logical split and balancing of the system. Both systems used a continuous supply system for components.Rapporten, som är en del av kursen "MG202X Examensarbete", har skrivits för institutionen Industriell Produktion på KTH under handledning av Antonio Maffei. Arbetet har inriktats på att utveckla ett monteringssystem hos Schneider Electrics produktionsanläggning i Nyköping. Författarna har delat upp rapporten mellan en litteraturstudie kring Lean produktion och monteringssystem, en analys av den aktuella situationen och en presentation av lösning. Litteraturstudien presenterar tre separata delar som bildar ramverket till vår analys. Den första delen är monteringssystem vilket beskriver olika typer av designalternativ samt vilka förluster som finns i ett monteringssystem. Vidare består den andra delen av Lean produktion där utvalda delar av filosofin har beskrivits. Den sista delen av studien behandlar utformandet av den enskilda arbetssituationen med hänsyn till ergonomi och komponentpresentation. Analysen av situationen hos Schneider Electric har gjorts under en tremånadersperiod inkluderat tidsanalyser, observationer och intervjuer. För att analysera dagens system krävdes en modell för uppskattning av monteringstider och arbetsbelastning. En djupgående förståelse av dagsläget var grunden för att skapa ett anpassat och accepterat monteringssystem. Arbetet resulterade i två förslag till monteringssystem. Det ena systemet bestod endast av en arbetsstation för enklare monteringsförfarande. Det andra systemet skall användas till mer komplexa produkter och har en högre kapacitet då den består av tre arbetsstationer. Då monteringssystemet består av tre skiljda arbetsstationer innebär detta att monteringsprocessen har delats vilket skedde genom att en avvägning mellan logisk delning och balansering. Båda systemen använde sig av ett kanbansystem för komponenttillförsel.
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Nordsveen, Espen T. "Mixed Integer Model Predictive Control of Multiple Shale Gas Wells." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for teknisk kybernetikk, 2012. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-18400.
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Horizontal wells with multistage hydraulic fracturing are today the most important drilling technology for shale gas extraction. Considered unprofitable before, the production has now become economically profitable due to advances in technology. Shales main characteristics is its low permeability, making the gas challenging and expensive to extract. Hydraulic fracturing stimulates the wells by creating additional conductivity, making the gas flows from storage pores to the well. This flow only possible in a short time scale, and states the need for multistage fracturing. Shale gas flow therefore exhibits a high initial peak, followed by a rapid decline in production rates. The use of shut-ins of shale gas wells allows for pressure build-up and may prevent liquid loading, as a means of boosting production. Shut-ins are used as on/off control variables in short-term model-based optimization of multiple shale gas wells with the objective of tracking a reference rate, while at the same time avoiding liquid loading. Previous work have focused on open-loop optimization. Here, an open-loop formulation is compared to a closed-loop formulation, in the form of mixed integer model predictive control. Both formulations are implemented in IBM ILOG CPLEX, with and without disturbances. Optimal production settings are solved in the presence of global constraints on production rates and minimal shut-in time. This allows for shut-ins with variable periods. The implementation is sensitive to initial conditions, horizons and weighting factors. The closed-loop formulation shows the best ability to reduce the effects of disturbances.