Relevant bibliographies by topics / Generalised linear modelling (GLM)

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Generalised linear modelling (GLM)'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Generalised linear modelling (GLM)"

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Nogués-Bravo, D. "Comparing regression methods to predict species richness patterns." Web Ecology 9, no. 1 (December 9, 2009): 58–67. http://dx.doi.org/10.5194/we-9-58-2009.

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Abstract. Multivariable regression models have been used extensively as spatial modelling tools. However, other regression approaches are emerging as more efficient techniques. This paper attempts to present a synthesis of Generalised Regression Models (Generalized Linear Models, GLMs, Generalized Additive Models, GAMs), and a Geographically Weighted Regression, GWR, implemented in a GAM, explaining their statistical formulations and assessing improvements in predictive accuracy compared with linear regressions. The problems associated with these approaches are also discussed. A digital databa
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Vencálek, Ondřej, Karel Hron, and Peter Filzmoser. "A comparison of generalised linear models and compositional models for ordered categorical data." Statistical Modelling 20, no. 3 (January 15, 2019): 249–73. http://dx.doi.org/10.1177/1471082x18816540.

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Ordered categorical data occur in many applied fields, such as geochemistry, econometrics, sociology and demography or even transportation research, for example, in the form of results from various questionnaires. There are different possibilities for modelling proportions of individual categories. Generalised linear models (GLMs) are traditionally used for this purpose, but also methods of compositional data analysis (CoDa) can be considered. Here, both approaches are compared in depth. Particularly, different assumptions of the models on variability are highlighted. Advantages and disadvanta
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Saavedra, Angeles, Javier Taboada, María Araújo, and Eduardo Giráldez. "Generalized Linear Spatial Models to Predict Slate Exploitability." Journal of Applied Mathematics 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/531062.

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The aim of this research was to determine the variables that characterize slate exploitability and to model spatial distribution. A generalized linear spatial model (GLSMs) was fitted in order to explore relationship between exploitability and different explanatory variables that characterize slate quality. Modelling the influence of these variables and analysing the spatial distribution of the model residuals yielded a GLSM that allows slate exploitability to be predicted more effectively than when using generalized linear models (GLM), which do not take spatial dependence into account. Study
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Bar Massada, Avi, Alexandra D. Syphard, Susan I. Stewart, and Volker C. Radeloff. "Wildfire ignition-distribution modelling: a comparative study in the Huron–Manistee National Forest, Michigan, USA." International Journal of Wildland Fire 22, no. 2 (2013): 174. http://dx.doi.org/10.1071/wf11178.

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Wildfire ignition distribution models are powerful tools for predicting the probability of ignitions across broad areas, and identifying their drivers. Several approaches have been used for ignition-distribution modelling, yet the performance of different model types has not been compared. This is unfortunate, given that conceptually similar species-distribution models exhibit pronounced differences among model types. Therefore, our goal was to compare the predictive performance, variable importance and the spatial patterns of predicted ignition-probabilities of three ignition-distribution mod
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Bekar, İsmail, and Çağatay Tavşanoğlu. "Modelling the drivers of natural fire activity: the bias created by cropland fires." International Journal of Wildland Fire 26, no. 10 (2017): 845. http://dx.doi.org/10.1071/wf16183.

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Wildland and cropland fires, which differ considerably in fire regime characteristics, have often been evaluated jointly to estimate regional or global fire regimes using satellite-based fire activity data. We hypothesised that excluding cropland fires will change the output of the models regarding the drivers of natural fire activity. We modelled MODIS fire activity data of western and southern Turkey for the years 2000–2015 using binomial generalised linear models in which many climatic, anthropogenic and geographic factors were included as predictor variables. For modelling, we used differe
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Osawa, Takeshi, Hiromune Mitsuhashi, Yuta Uematsu, and Atushi Ushimaru. "Bagging GLM: Improved generalized linear model for the analysis of zero-inflated data." Ecological Informatics 6, no. 5 (September 2011): 270–75. http://dx.doi.org/10.1016/j.ecoinf.2011.05.003.

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Lovegrove, Gordon R., and Tarek Sayed. "Macro-level collision prediction models for evaluating neighbourhood traffic safety." Canadian Journal of Civil Engineering 33, no. 5 (May 1, 2006): 609–21. http://dx.doi.org/10.1139/l06-013.

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This study describes the development of macro-level (i.e., neighbourhood or traffic zone level) collision prediction models using data from 577 neighbourhoods across the Greater Vancouver Regional District. The objective is to provide a safety planning decision-support tool that facilitates a proactive approach to community planning which addresses road safety before problems emerge. The models are developed using the generalized linear regression modelling (GLM) technique assuming a negative binomial error structure. The resulting models relate traffic collisions to neighbourhood characterist
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Karl, Florian M., Jennifer Smith, Shannon Piedt, Kate Turcotte, and Ian Pike. "Applying the health action process approach to bicycle helmet use and evaluating a social marketing campaign." Injury Prevention 24, no. 4 (August 5, 2017): 288–95. http://dx.doi.org/10.1136/injuryprev-2017-042399.

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BackgroundBicycle injuries are of concern in Canada. Since helmet use was mandated in 1996 in the province of British Columbia, Canada, use has increased and head injuries have decreased. Despite the law, many cyclists do not wear a helmet. Health action process approach (HAPA) model explains intention and behaviour with self-efficacy, risk perception, outcome expectancies and planning constructs. The present study examines the impact of a social marketing campaign on HAPA constructs in the context of bicycle helmet use.MethodA questionnaire was administered to identify factors determining hel
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Chun, K. P., H. S. Wheater, and C. J. Onof. "Streamflow estimation for six UK catchments under future climate scenarios." Hydrology Research 40, no. 2-3 (April 1, 2009): 96–112. http://dx.doi.org/10.2166/nh.2009.086.

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Possible changes in streamflow in response to climate variation are crucial for anthropological and ecological systems. However, estimates of precipitation under future climate scenarios are notoriously uncertain. In this article, rainfall time series are generated by the generalized linear model (GLM) approach in which stochastic time series are generated using alternative climate model output variables and potential evaporation series estimated by a temperature method. These have been input to a conceptual rainfall–runoff model (pd4-2par) to simulate the daily streamflows for six UK catchmen
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Iyit, Neslihan. "Modelling world energy security data from multinomial distribution by generalized linear model under different cumulative link functions." Open Chemistry 16, no. 1 (April 30, 2018): 377–85. http://dx.doi.org/10.1515/chem-2018-0053.

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AbstractEnergy securityis one of the major components of energy sustainability in the world’s energy performance. In this study,energy securityis taken as an ordinal response variable coming from the multinomial distribution with the energy grade levelsA,B,C, andD. Thereafter, the worldenergy securitydata is tried to be statistically modelled by usinggeneralized linear model (GLM)approach for the ordinal response variable under different cumulative link functions. The cumulative link functions comparatively used in this study are cumulative logit, cumulative probit, cumulative complementary lo
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Dissertations / Theses on the topic "Generalised linear modelling (GLM)"

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Nargis, Suraiya, and n/a. "Robust methods in logistic regression." University of Canberra. Information Sciences & Engineering, 2005. http://erl.canberra.edu.au./public/adt-AUC20051111.141200.

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My Masters research aims to deepen our understanding of the behaviour of robust methods in logistic regression. Logistic regression is a special case of Generalized Linear Modelling (GLM), which is a powerful and popular technique for modelling a large variety of data. Robust methods are useful in reducing the effect of outlying values in the response variable on parameter estimates. A literature survey shows that we are still at the beginning of being able to detect extreme observations in logistic regression analyses, to apply robust methods in logistic regression and to present informativel
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Hatzopoulos, Peter. "Statistical and mathematical modelling for mortality trends and the comparison of mortality experiences, through generalised linear models and GLIM." Thesis, City University London, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364032.

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Dixon, William J., and bill dixon@dse vic gov au. "Uncertainty in Aquatic Toxicological Exposure-Effect Models: the Toxicity of 2,4-Dichlorophenoxyacetic Acid and 4-Chlorophenol to Daphnia carinata." RMIT University. Biotechnology and Environmental Biology, 2005. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20070119.163720.

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Uncertainty is pervasive in risk assessment. In ecotoxicological risk assessments, it arises from such sources as a lack of data, the simplification and abstraction of complex situations, and ambiguities in assessment endpoints (Burgman 2005; Suter 1993). When evaluating and managing risks, uncertainty needs to be explicitly considered in order to avoid erroneous decisions and to be able to make statements about the confidence that we can place in risk estimates. Although informative, previous approaches to dealing with uncertainty in ecotoxicological modelling have been found to b
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Andersson, Gustaf. "Generalised linear factor score regression : a comparison of four methods." Thesis, Uppsala universitet, Statistiska institutionen, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-412851.

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Factor score regression has recently received growing interest as an alternative for structural equation modelling. Two issues causing uncertainty for researchers are addressed in this thesis. Firstly, more knowledge is needed on how different approaches to calculating factor score estimates compare when estimating factor score regression models. Secondly, many applications are left without guidance because of the focus on normally distributed outcomes in the literature. This thesis examines how factor scoring methods compare when estimating regression coefficients in generalised linear factor
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Stephenson, John. "Multilevel generalised linear modelling and competing risks multistate survival analysis modelling of childhood caries." Thesis, Cardiff University, 2009. http://eprints.hud.ac.uk/7910/.

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There has been an ongoing debate regarding appropriate strategies for the management of carious primary teeth. Studies appear to provide evidence that both selective, symptom-based interventions and traditional restorative strategies are advantageous. However, the analysis and quantification of childhood caries may be affected by clustering of data, and the concurrent risk of exfoliation of primary teeth. Multilevel generalised linear models for the occurrence of primary caries were derived utilising data from a cohort study of 2,654 children aged 4-5 years at baseline undertaken 1999-2003. Th
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Lelièvre, Stéphanie. "Identification et caractérisation des frayères hivernales en Manche Orientale et la partie sud de la mer du Nord : Identification des oeufs de poissons, cartographie et modélisation des habitats de ponte." Nantes, 2010. http://www.theses.fr/2010NANT2110.

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Une meilleure connaissance des frayères des principaux poissons commerciaux de la mer du Nord semble nécessaire pour leur surveillance. La composition et l'abondance des espèces d'oeufs collectés par le CUFES (Continuous Underway Fish Egg Sampler) sont comparées à celle collectées par le VET (Vertical Egg Trawl) permettant de prouver l'efficacité du CUFES en Manche et mer du Nord. L'identification des œufs de poissons principalement basée sur des critères morphologiques n'est pas toujours fiable. En effet, certaines espèces comme la morue (Gadus morhua) et le merlan (Merlangius merlangus) ont
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Books on the topic "Generalised linear modelling (GLM)"

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D, Payne C., and Numerical Algorithms Group, eds. The GLIM system release 3.77: Generalised Linear Interactive Modelling manual. Oxford: Numerical Algorithms Group, 1985.

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D, Payne C., and Numerical Algorithms Group, eds. The GLIM system release 3.77: Generalised Linear Interactive Modelling manual. Oxford: NumericalAlgorithms Group, 1986.

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Miles, Jeremy. General and generalised linear models. Oxford University Press, 2015. http://dx.doi.org/10.1093/med:psych/9780198527565.003.0017.

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This chapter discusses general and generalised linear models (GLM and GLZ respectively). It outlines GLMs (mean, properties of GLMs and the mean), samples and populations, comparison of two groups of data, multiple regression and the GLM, analysis of variance (ANOVA) and the GLM, GLM in SPSS, and the GLZ).
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(Editor), Brian Francis, Mick Green (Editor), and Clive Payne (Editor), eds. The GLIM System: Release 4 Manual (Generalized Linear Interactive Modelling). Oxford University Press, USA, 1993.

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Book chapters on the topic "Generalised linear modelling (GLM)"

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Quicke, Donald, Buntika A. Butcher, and Rachel Kruft Welton. "More generalized linear modelling." In Practical R for biologists: an introduction, 171–86. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789245349.0171.

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Abstract This chapter employs generalized linear modelling using the function glm when we know that variances are not constant with one or more explanatory variables and/or we know that the errors cannot be normally distributed, for example, they may be binary data, or count data where negative values are impossible, or proportions which are constrained between 0 and 1. A glm seeks to determine how much of the variation in the response variable can be explained by each explanatory variable, and whether such relationships are statistically significant. The data for generalized linear models take the form of a continuous response variable and a combination of continuous and discrete explanatory variables.
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Quicke, Donald, Buntika A. Butcher, and Rachel Kruft Welton. "More generalized linear modelling." In Practical R for biologists: an introduction, 171–86. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789245349.0015.

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Abstract This chapter employs generalized linear modelling using the function glm when we know that variances are not constant with one or more explanatory variables and/or we know that the errors cannot be normally distributed, for example, they may be binary data, or count data where negative values are impossible, or proportions which are constrained between 0 and 1. A glm seeks to determine how much of the variation in the response variable can be explained by each explanatory variable, and whether such relationships are statistically significant. The data for generalized linear models take the form of a continuous response variable and a combination of continuous and discrete explanatory variables.
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England, P. D., and R. J. Verrall. "Modelling Excess Mortality of Diabetics: Generalised Linear Models and Dynamic Estimation." In Advances in GLIM and Statistical Modelling, 78–84. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2952-0_13.

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Eilers, Paul H. C., and Brian D. Marx. "Generalized Linear Models with P-splines." In Advances in GLIM and Statistical Modelling, 72–77. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2952-0_12.

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Härdle, Wolfgang K., and Berwin A. Turlach. "Nonparametric Approaches to Generalized Linear Models." In Advances in GLIM and Statistical Modelling, 213–25. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2952-0_33.

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Czado, Claudia. "On Link Selection in Generalized Linear Models." In Advances in GLIM and Statistical Modelling, 60–65. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2952-0_10.

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Nakaya, Tomoki. "Geographically Weighted Generalised Linear Modelling." In Geocomputation: A Practical Primer, 200–220. 1 Oliver's Yard, 55 City Road London EC1Y 1SP: SAGE Publications, Inc., 2015. http://dx.doi.org/10.4135/9781473916432.n12.

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Frühwirth-Schnatter, Sylvia. "Approximate Predictive Integrals for Dynamic Generalized Linear Models." In Advances in GLIM and Statistical Modelling, 101–6. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2952-0_16.

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Fahrmeir, Ludwig, Wolfgang Hennevogl, and Karola Klemme. "Smoothing in dynamic generalized linear models by Gibbs sampling." In Advances in GLIM and Statistical Modelling, 85–90. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2952-0_14.

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Seeber, G. U. H. "Saddlepoint Approximations for Generalized Linear Models: A Gentle Introduction." In Advances in GLIM and Statistical Modelling, 195–200. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2952-0_30.

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Conference papers on the topic "Generalised linear modelling (GLM)"

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"Generalised linear model and analysis of cereal plant biomass." In 20th International Congress on Modelling and Simulation (MODSIM2013). Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2013. http://dx.doi.org/10.36334/modsim.2013.b1.cespedes.

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Wang, Kun, Ying Zhang, and Richard W. Jones. "The Modelling of Hysteresis in Magnetorheological Dampers Using a Generalised Prandtl-Ishlinskii Approach." In ASME 2010 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. ASMEDC, 2010. http://dx.doi.org/10.1115/smasis2010-3672.

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The major drawback of magnetorheological dampers (MR) lies in their non-linear and hysteretic force-velocity response. To take full advantage of the operating characteristics of these devices a high fidelity model is required for control analysis and design. In this contribution the ability of a generalised PI operator-based model to represent the characteristics of a commercially available MR damper is examined. This approach allows the user to define the PI operator to best match the hysteresis characteristics. For the MR damper the force-velcoity hysteresis characteristic is ‘S’ shaped and
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