Journal articles: 'Generalized Linear Modelling'

1

Wilson, K., and B. T. Grenfell. "Generalized linear modelling for parasitologists." Parasitology Today 13, no. 1 (January 1997): 33–38. http://dx.doi.org/10.1016/s0169-4758(96)40009-6.

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2

Atkinson, Peter, Hester Jiskoot, Remo Massari, and Tavi Murray. "Generalized linear modelling in geomorphology." Earth Surface Processes and Landforms 23, no. 13 (December 1998): 1185–95. http://dx.doi.org/10.1002/(sici)1096-9837(199812)23:13<1185::aid-esp928>3.0.co;2-w.

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3

Schuenemeyer, John H., P. McCullagh, and J. A. Nelder. "Generalized Linear Models." Technometrics 34, no. 2 (May 1992): 224. http://dx.doi.org/10.2307/1269238.

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4

Breslow, Norman, Ludwig Fahrmeir, and Gerhard Tutz. "Multivariate Statistical Modelling Based on Generalized Linear Models." Journal of the American Statistical Association 91, no. 434 (June 1996): 908. http://dx.doi.org/10.2307/2291687.

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5

Glosup, Jeffrey. "Multivariate Statistical Modelling Based on Generalized Linear Models." Technometrics 37, no. 3 (August 1995): 350–51. http://dx.doi.org/10.1080/00401706.1995.10484351.

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6

Chappell, Rick. "Multivariate Statistical Modelling Based on Generalized Linear Models." Journal of the American Statistical Association 98, no. 463 (September 2003): 776–77. http://dx.doi.org/10.1198/jasa.2003.s302.

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7

Ziegel, Eric. "Multivariate Statistical Modelling Based on Generalized Linear Models." Technometrics 44, no. 1 (February 2002): 94. http://dx.doi.org/10.1198/tech.2002.s673.

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8

LEE, JAMES, H. P. LEE, and K. S. CHIA. "Analysis of Registry Data by Generalized Linear Modelling." International Journal of Epidemiology 19, no. 2 (1990): 472–73. http://dx.doi.org/10.1093/ije/19.2.472.

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9

Sutton, G. R., and B. J. Moore. "Velocity modelling using a generalized linear inversion technique." Exploration Geophysics 16, no. 2-3 (June 1985): 287–88. http://dx.doi.org/10.1071/eg985287.

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10

Yunus, Rossita M., Masud M. Hasan, Nuradhiathy A. Razak, Yong Z. Zubairi, and Peter K. Dunn. "Modelling daily rainfall with climatological predictors: Poisson-gamma generalized linear modelling approach." International Journal of Climatology 37, no. 3 (June 10, 2016): 1391–99. http://dx.doi.org/10.1002/joc.4784.

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11

Heuberger, P. S. C., P. M. J. Van den Hof, and O. H. Bosgra. "Modelling Linear Dynamical Systems through Generalized Orthonormal Basis Functions." IFAC Proceedings Volumes 26, no. 2 (July 1993): 19–22. http://dx.doi.org/10.1016/s1474-6670(17)48214-0.

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12

FARRINGTON, C. P. "INTERVAL CENSORED SURVIVAL DATA: A GENERALIZED LINEAR MODELLING APPROACH." Statistics in Medicine 15, no. 3 (February 15, 1996): 283–92. http://dx.doi.org/10.1002/(sici)1097-0258(19960215)15:3<283::aid-sim171>3.0.co;2-t.

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13

Tammeraid, I. "GENERALIZED LINEAR METHODS AND CONVERGENCE ACCELERATION." Mathematical Modelling and Analysis 8, no. 4 (December 31, 2003): 329–35. http://dx.doi.org/10.3846/13926292.2003.9637234.

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14

Koper, Nicola, and Micheline Manseau. "Generalized estimating equations and generalized linear mixed-effects models for modelling resource selection." Journal of Applied Ecology 46, no. 3 (June 2009): 590–99. http://dx.doi.org/10.1111/j.1365-2664.2009.01642.x.

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15

Shao, Jing, and Fanwei Meng. "New Interval Oscillation Criteria for Certain Linear Hamiltonian Systems." Discrete Dynamics in Nature and Society 2013 (2013): 1–5. http://dx.doi.org/10.1155/2013/784279.

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Using a generalized Riccati transformation and the general integral means technique, some new interval oscillation criteria for the linear matrix Hamiltonian systemU'=(A(t)-λ(t)I)U+B(t)V,V'=C(t)U+(μ(t)I-A*(t))V,t≥t0are obtained. These results generalize and improve the oscillation criteria due to Zheng (2008). An example is given to dwell upon the importance of our results.

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16

Zhang, Zhongxin, and Holford Theodore. "Generalized conditionally linear models." Journal of Statistical Computation and Simulation 62, no. 1-2 (December 1998): 105–21. http://dx.doi.org/10.1080/00949659808811927.

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17

Von Tress, Mark. "Generalized, Linear, and Mixed Models." Technometrics 45, no. 1 (February 2003): 99. http://dx.doi.org/10.1198/tech.2003.s13.

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18

Alqallaf, Fatemah, and Claudio Agostinelli. "Robust Inference in Generalized Linear Models." Communications in Statistics - Simulation and Computation 45, no. 9 (August 2, 2016): 3053–73. http://dx.doi.org/10.1080/03610918.2014.911896.

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19

Smyth, Gordon K., and Arūnas P. Verbyla. "Adjusted likelihood methods for modelling dispersion in generalized linear models." Environmetrics 10, no. 6 (November 1999): 695–709. http://dx.doi.org/10.1002/(sici)1099-095x(199911/12)10:6<695::aid-env385>3.0.co;2-m.

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20

Aitkin, Murray. "Meta-analysis by random effect modelling in generalized linear models." Statistics in Medicine 18, no. 17-18 (September 15, 1999): 2343–51. http://dx.doi.org/10.1002/(sici)1097-0258(19990915/30)18:17/18<2343::aid-sim260>3.0.co;2-3.

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21

Lee, Youngjo, and Maengseok Noh. "Modelling random effect variance with double hierarchical generalized linear models." Statistical Modelling: An International Journal 12, no. 6 (December 2012): 487–502. http://dx.doi.org/10.1177/1471082x12460132.

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22

Rudy, Ashley C. A., Scott F. Lamoureux, Paul Treitz, and Karin Y. van Ewijk. "Transferability of regional permafrost disturbance susceptibility modelling using generalized linear and generalized additive models." Geomorphology 264 (July 2016): 95–108. http://dx.doi.org/10.1016/j.geomorph.2016.04.011.

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23

Myers, Donald E. "Linear and Generalized Linear Mixed Models and Their Applications." Technometrics 50, no. 1 (February 2008): 93–94. http://dx.doi.org/10.1198/tech.2008.s536.

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24

Liu, Shuxin, Haijun Jiang, Liwei Zhang, and Xuehui Mei. "Distributed Adaptive Optimization for Generalized Linear Multiagent Systems." Discrete Dynamics in Nature and Society 2019 (August 1, 2019): 1–10. http://dx.doi.org/10.1155/2019/9181093.

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In this paper, the edge-based and node-based adaptive algorithms are established, respectively, to solve the distribution convex optimization problem. The algorithms are based on multiagent systems with general linear dynamics; each agent uses only local information and cooperatively reaches the minimizer. Compared with existing results, a damping term in the adaptive law is introduced for the adaptive algorithms, which makes the algorithms more robust. Under some sufficient conditions, all agents asymptotically converge to the consensus value which minimizes the cost function. An example is provided for the effectiveness of the proposed algorithms.

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25

Christiansen, Marcus C., and Edo Schinzinger. "A CREDIBILITY APPROACH FOR COMBINING LIKELIHOODS OF GENERALIZED LINEAR MODELS." ASTIN Bulletin 46, no. 3 (May 24, 2016): 531–69. http://dx.doi.org/10.1017/asb.2016.11.

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AbstractGeneralized linear models are a popular tool for the modelling of insurance claims data. Problems arise with the model fitting if little statistical information is available. In case that related statistics are available, statistical inference can be improved with the help of the borrowing-strength principle. We present a credibility approach that combines the maximum likelihood estimators of individual canonical generalized linear models in a meta-analytic way to an improved credibility estimator. We follow the concept of linear empirical Bayes estimation, which reduces the necessary parametric assumptions to a minimum. The concept is illustrated by a simulation study and an application example from mortality modelling.

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26

LIU, PING, and CHANGAN LIU. "LINEAR GENERALIZED SYNCHRONIZATION OF SPATIAL JULIA SETS." International Journal of Bifurcation and Chaos 21, no. 05 (May 2011): 1281–91. http://dx.doi.org/10.1142/s0218127411029094.

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In this paper, the generalized synchronization of spatial Julia sets is investigated by applying linear coupling. We achieve the generalized synchronization of two different spatial Julia sets and also obtain the stable domain of the coupling strength based on the stable domains given of the spatial Julia sets. The relationship between the complex fixed plane and the synchronization of the spatial Julia sets is also analyzed and shows that the anticipative objective is accomplished. Finally, simulations are presented to demonstrate the effectiveness and feasibility of linear coupling.

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27

Ziegel, Eric R. "An Introduction to Generalized Linear Models." Technometrics 44, no. 4 (November 2002): 406–7. http://dx.doi.org/10.1198/tech.2002.s91.

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28

Glosup, Jeffrey. "Generalized Linear Models: An Applied Approach." Technometrics 47, no. 2 (May 2005): 232. http://dx.doi.org/10.1198/tech.2005.s254.

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29

Smyth, Gordon K., and Bent Jørgensen. "Fitting Tweedie's Compound Poisson Model to Insurance Claims Data: Dispersion Modelling." ASTIN Bulletin 32, no. 1 (May 2002): 143–57. http://dx.doi.org/10.2143/ast.32.1.1020.

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AbstractWe reconsider the problem of producing fair and accurate tariffs based on aggregated insurance data giving numbers of claims and total costs for the claims. Jørgensen and de Souza (Scand Actuarial J., 1994) assumed Poisson arrival of claims and gamma distributed costs for individual claims. Jørgensen and de Souza (1994) directly modelled the risk or expected cost of claims per insured unit, μ say. They observed that the dependence of the likelihood function on μ is as for a linear exponential family, so that modelling similar to that of generalized linear models is possible. In this paper we observe that, when modelling the cost of insurance claims, it is generally necessary to model the dispersion of the costs as well as their mean. In order to model the dispersion we use the framework of double generalized linear models. Modelling the dispersion increases the precision of the estimated tariffs. The use of double generalized linear models also allows us to handle the case where only the total cost of claims and not the number of claims has been recorded.

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30

Parkhurst, James M., Graeme Winter, David G. Waterman, Luis Fuentes-Montero, Richard J. Gildea, Garib N. Murshudov, and Gwyndaf Evans. "Robust background modelling inDIALS." Journal of Applied Crystallography 49, no. 6 (October 21, 2016): 1912–21. http://dx.doi.org/10.1107/s1600576716013595.

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A method for estimating the background under each reflection during integration that is robust in the presence of pixel outliers is presented. The method uses a generalized linear model approach that is more appropriate for use with Poisson distributed data than traditional approaches to pixel outlier handling in integration programs. The algorithm is most applicable to data with a very low background level where assumptions of a normal distribution are no longer valid as an approximation to the Poisson distribution. It is shown that traditional methods can result in the systematic underestimation of background values. This then results in the reflection intensities being overestimated and gives rise to a change in the overall distribution of reflection intensities in a dataset such that too few weak reflections appear to be recorded. Statistical tests performed during data reduction may mistakenly attribute this to merohedral twinning in the crystal. Application of the robust generalized linear model algorithm is shown to correct for this bias.

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31

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 database developed with Geographic Information Systems (GIS), including environmental maps and bird species richness distribution in northern Spain, is used for comparison of the techniques. GWR using splines has shown the highest improvement in accounted deviance when compared with traditional linear regression approach, followed by GAM and GLM.

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32

Meronen, Olga, and Ivar Tammeraid. "GENERALIZED LINEAR METHODS AND GAP TAUBERIAN REMAINDER THEOREMS." Mathematical Modelling and Analysis 13, no. 2 (June 30, 2008): 223–32. http://dx.doi.org/10.3846/1392-6292.2008.13.223-232.

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Some gap Tauberian remainder theorems are proved for generalized linear methods A = (Ank) , where Ank are bounded linear operators from Banach space X into X. In these theorems the investigated sequences have infinitely many constant pieces.

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33

Field, C. A., and M. A. Tingley. "Small sample intervals for generalized linear regression." Communications in Statistics - Simulation and Computation 22, no. 3 (January 1993): 689–707. http://dx.doi.org/10.1080/03610919308813117.

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34

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. Studying the residuals and comparing the prediction capacities of the two models lead us to conclude that the GLSM is more appropriate when the response variable presents spatial distribution.

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35

Luoto, Miska, and Jan Hjort. "Generalized linear modelling in periglacial studies: terrain parameters and patterned ground." Permafrost and Periglacial Processes 15, no. 4 (2004): 327–38. http://dx.doi.org/10.1002/ppp.482.

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36

SANTOS, EUGENE. "Modelling cyclicity and generalized cost-based abduction using linear constraint satisfaction." Journal of Experimental & Theoretical Artificial Intelligence 5, no. 4 (October 1993): 359–90. http://dx.doi.org/10.1080/09528139308953777.

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37

Wu, Changbao, and Bixia Xu. "Deflator selection and generalized linear modelling in market-based regression analyses." Applied Financial Economics 18, no. 21 (December 2008): 1739–53. http://dx.doi.org/10.1080/09603100701735904.

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38

Lovison, G., M. Sciandra, A. Tomasello, and S. Calvo. "Modeling Posidonia oceanica growth data: from linear to generalized linear mixed models." Environmetrics 22, no. 3 (December 29, 2010): 370–82. http://dx.doi.org/10.1002/env.1063.

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39

Jiao, Hong-Wei, San-Yang Liu, and Ying-Feng Zhao. "Effective algorithm for solving the generalized linear multiplicative problem with generalized polynomial constraints." Applied Mathematical Modelling 39, no. 23-24 (December 2015): 7568–82. http://dx.doi.org/10.1016/j.apm.2015.03.025.

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40

Sun, Mei, Changyan Zeng, and Lixin Tian. "Linear generalized synchronization between two complex networks." Communications in Nonlinear Science and Numerical Simulation 15, no. 8 (August 2010): 2162–67. http://dx.doi.org/10.1016/j.cnsns.2009.08.010.

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41

Wang, Chun-Feng, San-Yang Liu, and Pei-Ping Shen. "Global minimization of a generalized linear multiplicative programming." Applied Mathematical Modelling 36, no. 6 (June 2012): 2446–51. http://dx.doi.org/10.1016/j.apm.2011.09.002.

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42

Gerhard, Daniel. "Simultaneous Small Sample Inference for Linear Combinations of Generalized Linear Model Parameters." Communications in Statistics - Simulation and Computation 45, no. 8 (June 19, 2014): 2678–90. http://dx.doi.org/10.1080/03610918.2014.895836.

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43

Yosibash, Zohar, and Barna A. Szab�. "Generalized stress intensity factors in linear elastostatics." International Journal of Fracture 72, no. 3 (1995): 223–40. http://dx.doi.org/10.1007/bf00037312.

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44

Bolfarine, Heleno. "Finite population prediction under dynamic generalized linear models." Communications in Statistics - Simulation and Computation 17, no. 1 (January 1988): 187–207. http://dx.doi.org/10.1080/03610918808812656.

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45

Torabi, Mahmoud. "Likelihood Inference for Spatial Generalized Linear Mixed Models." Communications in Statistics - Simulation and Computation 44, no. 7 (December 10, 2014): 1692–701. http://dx.doi.org/10.1080/03610918.2013.824099.

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46

Harandi, S. Shams, and M. H. Alamatsaz. "A complementary generalized linear failure rate-geometric distribution." Communications in Statistics - Simulation and Computation 46, no. 1 (October 21, 2016): 679–703. http://dx.doi.org/10.1080/03610918.2014.950744.

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47

Ibáñez, M. V., M. Prades, and A. Simó. "Modelling municipal waste separation rates using generalized linear models and beta regression." Resources, Conservation and Recycling 55, no. 12 (October 2011): 1129–38. http://dx.doi.org/10.1016/j.resconrec.2011.07.002.

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48

Batson, S., AJ Sutton, and K. Abrams. "Comparison Of Generalized Linear Modelling Approaches As Applied To Network Meta-Analysis." Value in Health 19, no. 3 (May 2016): A101. http://dx.doi.org/10.1016/j.jval.2016.03.1724.

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49

Yau, Kelvin, Karen Yip, and H. K. Yuen. "Modelling repeated insurance claim frequency data using the generalized linear mixed model." Journal of Applied Statistics 30, no. 8 (October 2003): 857–65. http://dx.doi.org/10.1080/0266476032000075949.

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50

Narayana, C., B. Mahaboob, B. Venkateswarlu, J. Ravi sankar, and P. Balasiddamuni. "A Treatise on Testing General Linear Hypothesis in Stochastic Linear Regression Model." International Journal of Engineering & Technology 7, no. 4.10 (October 2, 2018): 539. http://dx.doi.org/10.14419/ijet.v7i4.10.21223.

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The main objective of this research article is to propose test statistics for testing general linear hypothesis about parameters in stochastics linear regression model using studentized residuals, RLS estimates and unrestricted internally studentized residuals. In 1998, M. Celia Rodriguez -Campos et.al [1] introduced a new test statistics to test the hypothesis of a generalized linear model in a regression context with random design. Li Cai et.al [2] provide a new test statistic for testing linear hypothesis in an OLS regression model that not assume homoscedasticity. P. Balasiddamuni et.al [3] proposed some advanced tools for mathematical and stochastical modelling.

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Journal articles on the topic 'Generalized Linear Modelling'

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