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作者： Christensen, Fletcher George William University of California - Irvine

学位级别：ET

Because the marginal densities corresponding to data modeled with generalized linear mixed models (GLMMs) usually lack closed-form expressions, model selection via existing tools like the deviance information criterion (DIC) can yield inconsistent results. We discuss why marginalization is preferable for the evaluation of competing mixed models, provide a new method for fast and accurate approximation of the marginal DIC for GLMMs, and demonstrate through simulation how numerical approximation of the DIC relative to our marginalization scheme gives more accurate model selection results than other numerical approximation methods for DIC. We also discuss some issues related to model selection in an analysis of longitudinal data collected to assess the effect of polycyclic aromatic hydrocarbons on hormone functioning in women who were attempting to get pregnant.

关键词： Statistics Biostatistics Environmental health DIC fertility marginalization missing data mixed modeling model selection

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Joint modeling of mixed Longitudinal Responses in Educational Research

Joint Modeling of Mixed Longitudinal Responses in Educationa...

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作者： Rummerfield, Wendy Nora University of California Irvine

学位级别：Ph.D., Doctor of Philosophy

The availability of assessment measurements that can be used effectively in pedagogical intervention studies is surprisingly limited. As a result, typical educational studies often report anecdotal evidence about the effectiveness of a new teaching method. It is difficult to perform a randomized experiment as students self-select into courses and breakout discussions based on their schedules. In the few studies that attempt to perform some kind of experiment, the efficacy is assessed by a single exam or possibly a pre- and post-test. However, consider the wealth of information collected during a typical course: homework, exams, clicker questions, as so on. The difficulty in analyzing data of this form is the lack of a natural joint distribution. While there may be some merit in looking into each outcome type separately, using all of the available data can answer new questions about how students learn throughout a course. This dissertation focuses on two areas of (statistics) education: discovering new pedagogical methods for teaching statistics at the introductory college level and creating novel methods to analyze such data. We motivate the need for our novel statistical methodology by discussing a pseudo-experiment performed in an introductory statistics course on using different simulation methods to teach sampling distributions. Due to the form of the available data, we were limited to three exams as our response, which changed our scientific question of interest. This prompted the development of two overarching methods for analyzing mixed response type longitudinal data. We also considered a second educational study, which provides more data on students through the learning management system (LMS), which is becoming more popular in education research. First, we examine a frequentist approach. We transform non-linear responses via the penalized quasi-likelihood technique and use Newton-Raphson and the expectation-maximization algorithm to estimate the fixed and

关键词： Higher education Longitudinal data mixed modeling Pedagogical intervention

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Hybrid model of multimodal based on data enhancement and lumped reaction kinetics: Applying to industrial ebullated-bed residue hydrogenation unit

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Chinese Journal of Chemical Engineering 2025年第2期78卷 284-302页

作者： Jian Long Mengru Zhang Anlan Li Cheng Huang Dong Xue Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of EducationEast China University of Science and TechnologyShanghai 200237China Sinopec Engineering Incorporation Beijing 100101China

Industrial ebullated-bed is an important device for promoting the cleaning and upgrading of oil products. The lumped kinetic model is a powerful tool for predicting the product yield of the ebullated-bed residue hydrogenation (EBRH) unit, However, during the long-term operation of the device, there are phenomena such as low frequency of material property analysis leading to limited operating data and diverse operating modes at the same time scale, which poses a huge challenge to building an accurate product yield prediction model. To address these challenges, a data augmentation-based eleven lumped reaction kinetics mechanism model was constructed. This model combines generative adversarial networks, outlier elimination, and L2 norm data filtering to expand the dataset and utilizes kernel principal component analysis-fuzzy C-means for operating condition partitioning. Based on the hydrogenation reaction mechanism, a single and sub operating condition eleven lumped reaction kinetics model of an ebullated-bed residue hydrogenation unit, comprising 55 reaction paths and 110 parameters, was constructed before and after data augmentation. Compared to the single model before data enhancement, the average absolute error of the sub-models under data enhancement division was reduced by 23%. Thus, these findings can help guide the operation and optimization of the production process.

关键词： mixed modeling Generative adversarial network Lumped kinetic model Multi-modal learning Ebullated-bed residue hydrogenation

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A Bayesian mixed effects support vector machine for learning and predicting daily substance use disorder patterns

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AMERICAN JOURNAL OF DRUG AND ALCOHOL ABUSE 2022年第4期48卷 413-421页

作者： Baurley, James W. Claus, Eric D. Witkiewitz, Katie McMahan, Christopher S. BioRealm LLC Data Sci Walnut CA USA Penn State Univ Dept Biobehav Hlth University Pk PA 16802 USA Univ New Mexico Dept Psychol Albuquerque NM 87131 USA Clemson Univ Sch Math & Stat Sci Clemson SC USA

Background Substance use disorder (SUD) is a heterogeneous disorder. Adapting machine learning algorithms to allow for the parsing of intrapersonal and interpersonal heterogeneity in meaningful ways may accelerate the discovery and implementation of clinically actionable interventions in SUD research. Objectives Inspired by a study of heavy drinkers that collected daily drinking and substance use (ABQ DrinQ), we develop tools to estimate subject-specific risk trajectories of heavy drinking;estimate and perform inference on patient characteristics and time-varying covariates;and present results in easy-to-use Jupyter notebooks. Methods We recast support vector machines (SVMs) into a Bayesian model extended to handle mixed effects. We then apply these methods to ABQ DrinQ to model alcohol use patterns. ABQ DrinQ consists of 190 heavy drinkers (44% female) with 109,580 daily observations. Results We identified male gender (point estimate;95% credible interval: -0.25;-0.29,-0.21), older age (-0.03;-0.03,-0.03), and time varying usage of nicotine (1.68;1.62,1.73), cannabis (0.05;0.03,0.07), and other drugs (1.16;1.01,1.35) as statistically significant factors of heavy drinking behavior. By adopting random effects to capture the subject-specific longitudinal trajectories, the algorithm outperforms traditional SVM (classifies 84% of heavy drinking days correctly versus 73%). Conclusions We developed a mixed effects variant of SVM and compare it to the traditional formulation, with an eye toward elucidating the importance of incorporating random effects to account for underlying heterogeneity in SUD data. These tools and examples are packaged into a repository for researchers to explore. Understanding patterns and risk of substance use could be used for developing individualized interventions.

关键词： Data science substance use disorder alcohol use disorder machine learning mixed modeling biomarker development Jupyter notebook

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Lateralized differences for verbal learning across trials in temporal lobe epilepsy are not affected by surgical intervention

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EPILEPSY & BEHAVIOR 2022年第0期128卷 108561-108561页

作者： Streese, Carolina Deifelt Manzel, Kenneth Wu, Zhengyuan Tranel, Daniel Carver Coll Med Dept Neurosurg 200 Hawkins Dr Iowa City IA 52242 USA Carver Coll Med Dept Neurol 200 Hawkins Dr Iowa City IA 52242 USA Univ Iowa Dept Psychol & Brain Sci 340 Iowa Ave Iowa City IA 52242 USA

This research aimed to broaden understanding of learning verbal material in participants with left- and right-sided mesial temporal lobe epilepsy (MTLE). We modeled word list-learning to determine how anterior temporal lobe resection affects verbal learning. Verbal learning (across trials) was assessed using the first five trials of the Rey Auditory Verbal Learning Test (RAVLT) in 128 participants with MTLE. mixedeffects modeling was used to determine whether learning curves differed between participants with left- and right-sided MTLE pre- and post- anterior temporal lobe resection. Laterality of MTLE had a significant effect on both the model intercept and the linear slope, whereby participants with leftsided MTLE retained fewer words on both the first trial and on each subsequent trial than participants with right-sided MTLE;and this held regardless of anterior temporal lobe resection status (t(117) = -3.516, p < .001;t(120.50) = -2.049, p = .042, for intercept and linear slope, respectively). There were no significant differences in the learning curves after anterior temporal lobe resection surgery in either left- or right-sided MTLE. Our findings suggest that acquisition of verbal information may be especially impaired in patients with left-sided MTLE. Further, we show that verbal learning across trials was not affected by surgical intervention. This finding contributes to the broader understanding of the impacts of anterior temporal lobe resection on verbal memory function, and has important implications for the clinical management and surgical planning for patients with temporal lobe epilepsy. Published by Elsevier Inc.

关键词： Temporal lobe resection mixed modeling Rey Auditory Verbal Learning Test AVLT Verbal memory Laterality

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Soft OR and Practice: The Contribution of the Founders of Operations Research

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OPERATIONS RESEARCH 2021年第3期69卷 727-738页

作者： Dyson, Robert G. O'Brien, Frances A. Shah, Devan B. Univ Warwick Warwick Business Sch Coventry CV4 7AL W Midlands England Broadwalk Ltd Thika 00100 Kenya

This paper explores the work of some 43 founders of operations research. In particular, it considers the links between soft operations research (OR) and these founders. Several of the founders were direct influencers of the soft OR proponents, whereas others related to the context, process, and content of soft OR. Coupled with the deductive and inductive reasoning approaches of soft OR, it is argued that soft OR is a legitimate branch of OR. The paper also focuses on the embeddedness of the founders, and the soft OR proponents, in practice and argues that, for academics, engagement with practice has been and will continue to be an important driver for the health and development of operations research.

关键词： practice problem structuring mixed modeling

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Longitudinal Dyadic Interdependence in Depression Symptoms of Caregivers Living with HIV in Uganda and Their Dependent Children's Neurodevelopment and Executive Behavior Outcomes

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AIDS AND BEHAVIOR 2021年第11期25卷 3828-3835页

作者： Familiar, Itziar Majumder, Atreyee Sikorskii, Alla Boivin, Michael Nakasujja, Noeline Bass, Judith Michigan State Univ Dept Psychiat 909 Wilson Rd A322 E Lansing MI 48824 USA Visiting Nurse Serv New York New York NY USA Michigan State Univ Dept Psychiat & Stat E Lansing MI 48824 USA Michigan State Univ Dept Psychiat E Lansing MI 48824 USA Michigan State Univ Dept Neurol E Lansing MI 48824 USA Makerere Univ Dept Psychiat Coll Hlth Sci Kampala Uganda Johns Hopkins Univ Bloomberg Sch Publ Hlth Dept Mental Hlth Baltimore MD USA

We tested a model of dyadic interdependence in depression symptoms experienced by female caregivers living with HIV in Uganda (n = 288) and behavioral problems of their HIV-infected (n = 92) and perinatally HIV-exposed uninfected (HEU) children (n = 196). Three repeated measures of caregiver depression symptoms and child neurodevelopment and behavioral outcomes were related to their own outcomes at a previous time point (actor effects), and the outcomes of the other member of the dyad (partner effects). Caregiver depression and child behavioral problem were interdependent over the 24 months of observation. Caregiver depression at T-n predicted child's behavioral problems at Tn+1 (coefficient = 0.1220, SE = 0.0313, p < 0.01);child behavioral problems at T-n predicted maternal depression at Tn+1 (coefficient = 0.0984, SE = 0.0253, p < 0.01). Results suggest the importance of services addressing behavioral needs of affected children and mental health of their mothers.

关键词： HIV Maternal depression Interdependence Child behavior Dyads mixed modeling

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modeling creatine-kinase MB concentrations following coronary artery bypass grafting

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Journal of Thoracic and Cardiovascular Surgery 2024年

作者： Romeo, Jamie L. Vriesendorp, Pieter A. Gerritsen, Kim Nader, Musafir Mahtab, Edris Maessen, Jos G. van‘t Hof, Arnoud W.J. Gollmann-Tepeköylü, Can van Rosmalen, Frank van der Horst, Iwan C.C. Mingels, Alma M.A. Heuts, Samuel Department of Cardiothoracic Surgery University Medical Center Maastricht University Maastricht Netherlands Cardiovascular Research Institute (CARIM) University Medical Center Maastricht University Maastricht Netherlands Department of Cardiology University Medical Center Maastricht University Maastricht Netherlands Department of Cardiothoracic Surgery Erasmus Medical Center Rotterdam Netherlands Department of Cardiology Zuyderland Medical Center Heerlen Netherlands Department of Cardiac Surgery Innsbruck Medical University Innsbruck Austria Department of Intensive Care Medicine University Medical Center Maastricht University Maastricht Netherlands Central Diagnostic Laboratory University Medical Center Maastricht University Maastricht Netherlands

Background: An increase in cardiac biomarkers is a prerequisite for diagnosing periprocedural myocardial infarction (PMI) after coronary artery bypass grafting (CABG). Early-phase risk detection may be aided by modeling time-dependent serum creatine kinase-MB (CK-MB) concentrations. This study aimed to model the kinetics of CK-MB while identifying its influencing factors. Methods: Patients who underwent elective CABG and had CK-MB measurements within 72 hours postoperatively were included. The primary outcome was the modeled post hoc kinetics of CK-MB in patients without potential PMI. These patients were defined as having no potential PMI based on the absence of ischemic electrocardiographic abnormalities, imaging abnormalities, in-hospital cardiac arrest, mortality, or postoperative unplanned catheterization. A web-based application was created using mixed-effect modeling to provide an interactive and individualized result. Results: A total of 1589 CK-MB measurements from 635 patients who underwent elective isolated CABG were available for analysis. Of these, 609 patients (96%) had no potential PMI and 26 (4%) had potential PMI. Male sex, aortic cross-clamp time, and cardioplegia type significantly impacted CK-MB concentrations. The diagnostic accuracy of the model had an area under the receiver operating characteristic curve of 82.8% (95% confidence interval, 72.6%-90.2%). A threshold of 7 μg/L yielded a sensitivity of 94% and a specificity of 80% (positive predictive value, 17%;negative predictive value, 99%) for excluding potential PMI in our study population. Conclusions: CK-MB release after CABG depends on the timing of measurement, patient sex, aortic cross-clamp time, and cardioplegia type. The model (available at https://***/ckmb) can be validated, reproduced, refined, and applied to other biomarkers. © 2024 The Authors

关键词： cardiac biomarkers coronary artery bypass grafting MB isoenzyme of creatine kinase (CK-MB) mixed modeling periprocedural myocardial infarction risk model

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Multivaluedness in Networks: Theory

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2020年第12期67卷 3462-3466页

作者： van Wyk, Michael Antonie Univ Witwatersrand Sch Elect & Informat Engn Johannesburg South Africa City Univ Hong Kong Hong Kong Peoples R China

This brief note reports the fundamental phenomenon of implicit multivaluedness exhibited from one output to the other of two node-systems with a common input-referred to as counter-cascaded(1) systems-under the appropriate conditions. The novel concepts of immanence and transcendence are introduced upon which the formulation and prove of a necessary and sufficient condition for multivaluedness are based;this is the main result of this note. Next, subsequent consequences of this result are presented. Among these is the fact that this result also holds for cascaded generalized systems. The novel application of structural complexity reduction in directed networks presented next, demonstrates the utility of multivaluedness and is itself a contribution to the theory of signals and systems. The significance of this brief presented here is that it contributes toward the theory of systems and networks as well as toward the arsenal of tools for studying networks.

关键词： Circuits and systems Neural networks Complexity theory Tools Indexes Distributed databases Big data cascaded systems complex networks counter-cascaded systems distributed measurement systems functional uniformization immanence mixed modeling multivaluedness multivalued function multivalued relation network analysis network science networked systems neural networks node rationalization nonlinear systems structural reduction transcendence single-valuedness single-valued relation well-defined mapping

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Data science at farm level: Explaining and predicting within-farm variability in potato growth and yield

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EUROPEAN JOURNAL OF AGRONOMY 2021年 123卷 126220-126220页

作者： Mulders, Puck J. A. M. van den Heuvel, Edwin R. van den Borne, Jacob van de Molengraft, Rene Heemels, W. P. M. H. (Maurice) Reidsma, Pytrik Eindhoven Univ Technol Dept Mech Engn Control Syst Technol POB 513 NL-5600 MB Eindhoven Netherlands Eindhoven Univ Technol Dept Comp Sci & Math Stochast POB 513 NL-5600 MB Eindhoven Netherlands Van den Borne Aardappelen Postelsedijk 15 NL-5541 NM Reusel Netherlands Wageningen Univ Plant Prod Syst Grp POB 430 NL-6700 AK Wageningen Netherlands

The growth and yield of crops within a farm largely vary among fields. Farms are increasing in size by acquiring smaller land parcels from different farmers who have different management strategies. As a result, between-field variability increases and understanding such variability is a necessity for precision farming. New data analysis techniques are needed in this context, especially given the trend that more farms are collecting more data. Therefore, this study has the objective to provide a data analysis methodology to analyze within-year variability and identify year-independent factors that influence growth. As a second objective, we applied this novel methodology to a case study, where we analyzed potato growth data of four successive years of a farm in the south of the Netherlands. The methodology consists of three main steps: (1) describing growth using mixed models, (2) clustering and explaining growth by correlating the clusters to (a) yield, (b) other plant characteristics and (c) to defining, limiting and reducing variables, and (3) predicting growth by automatically selecting a regression model. By applying our method on the potato growth data, we obtained the following results. The main results of the work are: (1) the estimated growth curves of the stems, haulm and tubers explain the between-field variability in growth well very well (R-2 of 0.85, 0.74 and 0.89, respectively), (2) clusters with a stem length between 110 and 130 cm have the highest average yield, (3) deeper groundwater level and sugar beet or grass as previously cultivated crop positively influence growth, and (4) N and K fertilization must be adjusted for optimal growth. Concluding, this study responds to the quest for new data-based methods for sustainable intensification, and is the first to explicitly analyze and explain differences in crop growth between fields in practice. In addition, clear management advice could be provided, showing the scientific and practical potential of o

关键词： Yield variability Growth modeling Potato The Netherlands mixed modeling Within-farm

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