Global ETD Search

11	Bayesian D-Optimal Design Issues and Optimal Design Construction Methods for Generalized Linear Models with Random Blocks January 2015 (has links) abstract: Optimal experimental design for generalized linear models is often done using a pseudo-Bayesian approach that integrates the design criterion across a prior distribution on the parameter values. This approach ignores the lack of utility of certain models contained in the prior, and a case is demonstrated where the heavy focus on such hopeless models results in a design with poor performance and with wild swings in coverage probabilities for Wald-type confidence intervals. Design construction using a utility-based approach is shown to result in much more stable coverage probabilities in the area of greatest concern. The pseudo-Bayesian approach can be applied to the problem of optimal design construction under dependent observations. Often, correlation between observations exists due to restrictions on randomization. Several techniques for optimal design construction are proposed in the case of the conditional response distribution being a natural exponential family member but with a normally distributed block effect . The reviewed pseudo-Bayesian approach is compared to an approach based on substituting the marginal likelihood with the joint likelihood and an approach based on projections of the score function (often called quasi-likelihood). These approaches are compared for several models with normal, Poisson, and binomial conditional response distributions via the true determinant of the expected Fisher information matrix where the dispersion of the random blocks is considered a nuisance parameter. A case study using the developed methods is performed. The joint and quasi-likelihood methods are then extended to address the case when the magnitude of random block dispersion is of concern. Again, a simulation study over several models is performed, followed by a case study when the conditional response distribution is a Poisson distribution. / Dissertation/Thesis / Doctoral Dissertation Industrial Engineering 2015 Statistics Industrial engineering Experimental Design GLM GLMM Random Block Random Intercept
12	Accounting for potential nonlinearity between catch and effort using meta-analysis and applying GLM and GLMM to fishing data from deployments of fixed and mobile gear Aljafary, Michelle 12 April 2016 (has links) My thesis examines nonlinearity between catch and effort. I use a meta-analysis of published literature and generalized linear mixed-effects models (GLMM) on both fixed and mobile gear fisheries of Atlantic Canada. The meta-analysis examines the proportionality of catch to effort using the slope of the reduced major axis (RMA) log-log regression, which accounts for “errors-in-variables”. The GLMMs explored proportionality while accounting for variation among fishing vessels. Both analyses found evidence for disproportionality between catch and effort. Catch that increases disproportionally to effort could result from either facilitation or recruitment of effort into the fishery. Catch increases that are less than proportional are expected from competitive interactions among fishers or gear saturation. The GLMM also revealed that the level of aggregation (by set, trip, monthly, or annually) can affect the apparent proportionality between catch and effort. In general, catch and effort should not be considered to be proportional. / May 2016
13	Cyclic di-AMP homeostasis and osmoregulation in Listeria monocytogenes Gibhardt, Johannes 31 March 2020 (has links) No description available. 570 Listeria monocytogenes c-di-AMP osmoregulation diadenylate cyclase potassium transporter cdaA cdaR glmM disA regulation Biologie (PPN619462639)
14	Investigating the Effect of Mechanical Beach Cleaning on Nesting, Hatching and Emergence Success of Loggerhead (Caretta caretta) and Green (Chelonia mydas) Sea Turtles in Broward County, Florida Earney, Megan A 28 July 2017 (has links) Sea turtles face many threats to their populations globally. Loggerhead sea turtles (Caretta caretta) and green sea turtles (Chelonia mydas) are listed by the International Union for the Conservation of Nature Red List as Endangered. In Florida, loggerhead and green sea turtles nest along the coastline during April-September. Mechanical beach cleaning is an aesthetic service performed daily on some beaches in Florida to clean the wrack line and/or the entire beach of debris. Alterations made to beaches by methods such as mechanical beach cleaning have the potential to impact sea turtle nesting, hatching, and emergence success. Generalized linear mixed models were performed to investigate the impacts of mechanical beach cleaning on nesting, hatching and emergence success of loggerhead and green turtles from 1997-2015 in Broward County, Florida. The results showed mechanical beach cleaning had an effect on nesting success, however, hatching and emergence success were not affected by mechanical beach cleaning. These results indicate that mechanical beach cleaning cannot solely be used to determine sea turtle management or conservation guidelines in Broward County. loggerhead sea turtle green sea turtle GLMM nesting success hatching success emergence success Broward County. Marine Biology
15	The Ecological Impacts of Non-Native Annual and Native Perennial Floral Insectaries on Beneficial Insect Activity Density and Arthropod-Mediated Ecosystem Services Within Ohio Pumpkin (<i>Cucurbita pepo</i>) Agroecosystems Phillips, Benjamin W. 15 October 2013 (has links) No description available. Entomology Agriculture Ecology pollination biological control pumpkin Cucurbita pepo floral strips habitat management annual insectary perennial insectary Ohio general linear mixed modeling glmm
16	Validierung des Sanierungsfortschrittes in der Paratuberkulosebekämpfung eines ausgewählten Milchviehbestandes bei Einsatz serologischer Diagnostikverfahren. / Surveillance and control of paratuberculosis in a dairy herd based on serological methods. Karapetyan, Artsrun 18 November 2009 (has links) No description available. 630 Landwirtschaft Veterinärmedizin Agricultural Sciences Paratuberkulose Sanierung Bekämpfung Serologie ELISA PCR GLMM Korrelation Johnes' disease paratuberculosis surveillance eradication serology ELISA PCR GLMM correlation 46.30 46.50 46.20 46.52 46.54 46.80 Bakteriologie YNW YNI 000: Diagnostik {Tiermedizin}
17	How Well Can Saliency Models Predict Fixation Selection in Scenes Beyond Central Bias? A New Approach to Model Evaluation Using Generalized Linear Mixed Models Nuthmann, Antje, Einhäuser, Wolfgang, Schütz, Immo 22 January 2018 (has links) (PDF) Since the turn of the millennium, a large number of computational models of visual salience have been put forward. How best to evaluate a given model's ability to predict where human observers fixate in images of real-world scenes remains an open research question. Assessing the role of spatial biases is a challenging issue; this is particularly true when we consider the tendency for high-salience items to appear in the image center, combined with a tendency to look straight ahead (“central bias”). This problem is further exacerbated in the context of model comparisons, because some—but not all—models implicitly or explicitly incorporate a center preference to improve performance. To address this and other issues, we propose to combine a-priori parcellation of scenes with generalized linear mixed models (GLMM), building upon previous work. With this method, we can explicitly model the central bias of fixation by including a central-bias predictor in the GLMM. A second predictor captures how well the saliency model predicts human fixations, above and beyond the central bias. By-subject and by-item random effects account for individual differences and differences across scene items, respectively. Moreover, we can directly assess whether a given saliency model performs significantly better than others. In this article, we describe the data processing steps required by our analysis approach. In addition, we demonstrate the GLMM analyses by evaluating the performance of different saliency models on a new eye-tracking corpus. To facilitate the application of our method, we make the open-source Python toolbox “GridFix” available. Psychophysik Aufmerksamkeit Visuelle Aufmerksamkeit Visuelle Wahrnehmung Augenbewegung Blickbewegung Technische Universität Chemnitz Publikationsfonds naturalistic scenes eye movements saliency models evaluation metrics fixation probability GLMM Chemnitz University of Technology Publication funds ddc:152 Psychophysik Aufmerksamkeit Visuelle Aufmerksamkeit Visuelle Wahrnehmung Augenbewegung Blickbewegung
18	How Well Can Saliency Models Predict Fixation Selection in Scenes Beyond Central Bias? A New Approach to Model Evaluation Using Generalized Linear Mixed Models Nuthmann, Antje, Einhäuser, Wolfgang, Schütz, Immo 22 January 2018 (has links) Since the turn of the millennium, a large number of computational models of visual salience have been put forward. How best to evaluate a given model's ability to predict where human observers fixate in images of real-world scenes remains an open research question. Assessing the role of spatial biases is a challenging issue; this is particularly true when we consider the tendency for high-salience items to appear in the image center, combined with a tendency to look straight ahead (“central bias”). This problem is further exacerbated in the context of model comparisons, because some—but not all—models implicitly or explicitly incorporate a center preference to improve performance. To address this and other issues, we propose to combine a-priori parcellation of scenes with generalized linear mixed models (GLMM), building upon previous work. With this method, we can explicitly model the central bias of fixation by including a central-bias predictor in the GLMM. A second predictor captures how well the saliency model predicts human fixations, above and beyond the central bias. By-subject and by-item random effects account for individual differences and differences across scene items, respectively. Moreover, we can directly assess whether a given saliency model performs significantly better than others. In this article, we describe the data processing steps required by our analysis approach. In addition, we demonstrate the GLMM analyses by evaluating the performance of different saliency models on a new eye-tracking corpus. To facilitate the application of our method, we make the open-source Python toolbox “GridFix” available. info:eu-repo/classification/ddc/152 ddc:152
19	Einfluss von transkraniellen Wechselstromstimulationen im Thetabereich auf die Bearbeitung der Stroop-Aufgabe / The influence of transcranial alternating current stimulation within the theta-range on performance in the stroop task Siegle, Micha Benjamin 31 December 1100 (has links) No description available. 610 transkranielle Wechselstromstimulation transkranielle Stimulation Stroop Aufgabe Stroop Effekt Gratton Effekt kognitive Kontrolle Neuromodulation ereigniskorrelierte Potentiale ereigniskorrelierte Synchronisation ereigniskorrelierte Desynchronisation Neurowissenschaften tACS transcranial stimulation Stroop task Stroop effect Gratton effect cognitive control neuromodulation dorsal anterior cingulate cortex (dACC) event-related potentials event-related synchronisation event-related desynchronisation neuroscience generalized linear mixed model (GLMM)

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