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Relevant bibliographies by topics / Vector generalized linear models

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Academic literature on the topic 'Vector generalized linear models'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Vector generalized linear models"

1

Yee, Thomas W., and Trevor J. Hastie. "Reduced-rank vector generalized linear models." Statistical Modelling: An International Journal 3, no. 1 (2003): 15–41. http://dx.doi.org/10.1191/1471082x03st045oa.

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Yee, Thomas W. "Reduced-rank vector generalized linear models with two linear predictors." Computational Statistics & Data Analysis 71 (March 2014): 889–902. http://dx.doi.org/10.1016/j.csda.2013.01.012.

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Yee, Thomas W., and Alec G. Stephenson. "Vector generalized linear and additive extreme value models." Extremes 10, no. 1-2 (2007): 1–19. http://dx.doi.org/10.1007/s10687-007-0032-4.

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She, Yiyuan. "Reduced rank vector generalized linear models for feature extraction." Statistics and Its Interface 6, no. 2 (2013): 197–209. http://dx.doi.org/10.4310/sii.2013.v6.n2.a4.

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Falk, Michael, Martin Hofmann, and Maximilian Zott. "On generalized max-linear models and their statistical interpolation." Journal of Applied Probability 52, no. 03 (2015): 736–51. http://dx.doi.org/10.1017/s0021900200113403.

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We propose a method to generate a max-stable process in C[0, 1] from a max-stable random vector in R d by generalizing the max-linear model established by Wang and Stoev (2011). For this purpose, an interpolation technique that preserves max-stability is proposed. It turns out that if the random vector follows some finite-dimensional distribution of some initial max-stable process, the approximating processes converge uniformly to the original process and the pointwise mean-squared error can be represented in a closed form. The obtained results carry over to the case of generalized Pareto proc

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Falk, Michael, Martin Hofmann, and Maximilian Zott. "On generalized max-linear models and their statistical interpolation." Journal of Applied Probability 52, no. 3 (2015): 736–51. http://dx.doi.org/10.1239/jap/1445543843.

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We propose a method to generate a max-stable process in C[0, 1] from a max-stable random vector in Rd by generalizing the max-linear model established by Wang and Stoev (2011). For this purpose, an interpolation technique that preserves max-stability is proposed. It turns out that if the random vector follows some finite-dimensional distribution of some initial max-stable process, the approximating processes converge uniformly to the original process and the pointwise mean-squared error can be represented in a closed form. The obtained results carry over to the case of generalized Pareto proce

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Matías, J. M., C. Ordóñez, J. Taboada, and T. Rivas. "Functional support vector machines and generalized linear models for glacier geomorphology analysis." International Journal of Computer Mathematics 86, no. 2 (2009): 275–85. http://dx.doi.org/10.1080/00207160801965305.

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Barahona, Sonia, Pablo Centella, Ximo Gual-Arnau, M. Victoria Ibáñez, and Amelia Simó. "Generalized linear models for geometrical current predictors: An application to predict garment fit." Statistical Modelling 20, no. 6 (2019): 562–91. http://dx.doi.org/10.1177/1471082x19885465.

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The aim of this article is to model an ordinal response variable in terms of vector-valued functional data included on a vector-valued reproducing kernel Hilbert space (RKHS). In particular, we focus on the vector-valued RKHS obtained when a geometrical object (body) is characterized by a current and on the ordinal regression model. A common way to solve this problem in functional data analysis is to express the data in the orthonormal basis given by decomposition of the covariance operator. But our data present very important differences with respect to the usual functional data setting. On t

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MATSUBA, IKUO. "GENERALIZED INFORMATION CRITERION FOR LINEAR AND NONLINEAR PROCESSES." International Journal of Bifurcation and Chaos 12, no. 02 (2002): 389–95. http://dx.doi.org/10.1142/s0218127402004413.

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A generalized information criterion is proposed to determine an embedding dimension and a delay time for delay coordinates of the reconstructed dynamics both for linear stochastic and nonlinear deterministic processes. While the standard maximum likelihood type method requires statistical parametric models such as autoregressive models, the generalized information criterion is constructed from the quantity in accordance with the second-order Renyi entropy in terms of the correlation integral for the finite number of data which is directly obtained from a time delay vector. It is found numerica

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Bourouis, Sami, Roobaea Alroobaea, Saeed Rubaiee, Murad Andejany, and Nizar Bouguila. "Nonparametric Bayesian Learning of Infinite Multivariate Generalized Normal Mixture Models and Its Applications." Applied Sciences 11, no. 13 (2021): 5798. http://dx.doi.org/10.3390/app11135798.

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This paper addresses the problem of data vectors modeling, classification and recognition using infinite mixture models, which have been shown to be an effective alternative to finite mixtures in terms of selecting the optimal number of clusters. In this work, we propose a novel approach for localized features modelling using an infinite mixture model based on multivariate generalized Normal distributions (inMGNM). The statistical mixture is learned via a nonparametric MCMC-based Bayesian approach in order to avoid the crucial problem of model over-fitting and to allow uncertainty in the numbe

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Dissertations / Theses on the topic "Vector generalized linear models"

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Butturi-Gomes, Davi. "Métodos estatísticos aplicados ao teste de Salmonella/microssoma: modelos, seleção e suas implicações." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-08032016-145021/.

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O teste de Salmonella/microssoma é um ensaio biológico amplamente utilizado para avaliar o potencial mutagênico de substâncias que podem colocar em risco a saúde humana e a qualidade ambiental. A variável resposta é constituída pela contagem do número de colônias revertentes em cada placa, entretanto geralmente há dois efeitos confundidos, o de toxicidade e o de mutagenicidade. Alguns modelos foram propostos para a análise dos dados desses experimentos, que nem sempre apresentam bons ajustes e não consideram explicitamente interações. Há, ainda, poucas plataformas computacionais disponíveis qu

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Göteman, Malin. "The Complex World of Superstrings : On Semichiral Sigma Models and N=(4,4) Supersymmetry." Doctoral thesis, Uppsala universitet, Teoretisk fysik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-183407.

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Non-linear sigma models with extended supersymmetry have constrained target space geometries, and can serve as effective tools for investigating and constructing new geometries. Analyzing the geometrical and topological properties of sigma models is necessary to understand the underlying structures of string theory. The most general two-dimensional sigma model with manifest N=(2,2) supersymmetry can be parametrized by chiral, twisted chiral and semichiral superfields. In the research presented in this thesis, N=(4,4) (twisted) supersymmetry is constructed for a semichiral sigma model. It is fo

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Hargell, Joanna. "Supervised Learning for Prediction of Tumour Mutational Burden." Thesis, KTH, Matematisk statistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-291801.

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Tumour Mutational Burden is a promising biomarker to predict response to immunotherapy. In this thesis, statistical methods of supervised learning were used to predict TMB: GLM, Decision Trees and SVM. Predictions were based on data from targeted DNA sequencing, using variants found in the exonic, intronic, UTR and intergenic regions of the human DNA. This project was of an exploratory nature, performed in a pan-cancer setting. Both regression and classification were considered. The purpose was to investigate whether variants found in these regions of the DNA sequence are useful when predictin

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Benghiat, Sonia. "Diagnostics for generalized linear models." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ64046.pdf.

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Creagh-Osborne, Jane. "Latent variable generalized linear models." Thesis, University of Plymouth, 1998. http://hdl.handle.net/10026.1/1885.

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Generalized Linear Models (GLMs) (McCullagh and Nelder, 1989) provide a unified framework for fixed effect models where response data arise from exponential family distributions. Much recent research has attempted to extend the framework to include random effects in the linear predictors. Different methodologies have been employed to solve different motivating problems, for example Generalized Linear Mixed Models (Clayton, 1994) and Multilevel Models (Goldstein, 1995). A thorough review and classification of this and related material is presented. In Item Response Theory (IRT) subjects are tes

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Mackinnon, Murray J. "Collinearity in generalized linear models." Thesis, University of British Columbia, 1986. http://hdl.handle.net/2429/25711.

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The concept of collinearity for generalized linear models is introduced and compared to that for standard linear models. Two approaches for detecting collinearity are presented and shown to lead to the same diagnostic procedure. These are analysed for the Poisson, gamma, inverse Gaussian, pth order, binomial proportion and negative binomial models. A bound is derived for the degree of collinearity in a generalized linear model in terms of that of the standard linear model. Estimation methods based on ridge, prior likelihood and principal components are proposed, and briefly illustrated with a

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Výtvar, Jaromír. "Dolovací modul systému pro dolování z dat na platformě NetBeans." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2010. http://www.nusl.cz/ntk/nusl-235551.

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The aim of this work is to get basic overview about the process of obtaining knowledge from databases - datamining and to analyze the datamining system developed at FIT BUT on the NetBeans platform in order to create a new mining module. We decided to implement a module for mining outliers and to extend existing regression module with multiple linear regression using generalized linear models. New methods using existing methods of Oracle Data Mining.

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Stroinski, Krzysztof Jerzy. "Generalized linear models in motor insurance." Thesis, Heriot-Watt University, 1987. http://hdl.handle.net/10399/1044.

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Jiang, Dingfeng. "Concave selection in generalized linear models." Diss., University of Iowa, 2012. https://ir.uiowa.edu/etd/2902.

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A family of concave penalties, including the smoothly clipped absolute deviation (SCAD) and minimax concave penalties (MCP), has been shown to have attractive properties in variable selection. The computation of concave penalized solutions, however, is a difficult task. We propose a majorization minimization by coordinate descent (MMCD) algorithm to compute the solutions of concave penalized generalized linear models (GLM). In contrast to the existing algorithms that uses local quadratic or local linear approximation of the penalty, the MMCD majo

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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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Books on the topic "Vector generalized linear models"

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Yee, Thomas W. Vector Generalized Linear and Additive Models. Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2818-7.

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A, Nelder John, ed. Generalized linear models. 2nd ed. Chapman and Hall, 1989.

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Myers, Raymond H., Douglas C. Montgomery, G. Geoffrey Vining, and Timothy J. Robinson. Generalized Linear Models. John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470556986.

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McCullagh, P., and J. A. Nelder. Generalized Linear Models. Springer US, 1989. http://dx.doi.org/10.1007/978-1-4899-3242-6.

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Gill, Jeff. Generalized Linear Models. SAGE Publications, Inc., 2001. http://dx.doi.org/10.4135/9781412984348.

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Gilchrist, Robert, Brian Francis, and Joe Whittaker, eds. Generalized Linear Models. Springer US, 1985. http://dx.doi.org/10.1007/978-1-4615-7070-7.

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Conference Board of the Mathematical Sciences. and National Science Foundation (U.S.), eds. Generalized linear mixed models. Institute of Mathematical Statistics, 2003.

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Applying generalized linear models. Springer, 1997.

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McCulloch, Charles E. Generalized Linear Mixed Models. Institute of Mathematical Statistics and American Statistical Association, 2003. http://dx.doi.org/10.1214/cbms/1462106059.

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McCulloch, Charles E. Generalized, Linear, and Mixed Models. John Wiley & Sons, Ltd., 2005.

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Book chapters on the topic "Vector generalized linear models"

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Yee, Thomas W. "Erratum: Vector Generalized Linear and Additive Models." In Springer Series in Statistics. Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2818-7_19.

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Song, Peter X. K., Mingyao Li, and Peng Zhang. "Vector Generalized Linear Models: A Gaussian Copula Approach." In Copulae in Mathematical and Quantitative Finance. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35407-6_12.

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Bapat, R. B. "Generalized Inverses." In Linear Algebra and Linear Models. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2739-0_4.

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Venables, W. N., and B. D. Ripley. "Generalized Linear Models." In Modern Applied Statistics with S-Plus. Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4899-2819-1_7.

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Atkinson, Anthony, and Marco Riani. "Generalized Linear Models." In Springer Series in Statistics. Springer New York, 2000. http://dx.doi.org/10.1007/978-1-4612-1160-0_6.

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Hilbe, Joseph M. "Generalized Linear Models." In International Encyclopedia of Statistical Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_273.

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Kroese, Dirk P., and Joshua C. C. Chan. "Generalized Linear Models." In Statistical Modeling and Computation. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8775-3_9.

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Venables, W. N., and B. D. Ripley. "Generalized Linear Models." In Statistics and Computing. Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4757-2719-7_7.

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Vittinghoff, Eric, David V. Glidden, Stephen C. Shiboski, and Charles E. McCulloch. "Generalized Linear Models." In Regression Methods in Biostatistics. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-1353-0_8.

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Islam, M. Ataharul, and Rafiqul I. Chowdhury. "Generalized Linear Models." In Analysis of Repeated Measures Data. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3794-8_4.

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Conference papers on the topic "Vector generalized linear models"

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Schniter, Philip, Sundeep Rangan, and Alyson K. Fletcher. "Vector approximate message passing for the generalized linear model." In 2016 50th Asilomar Conference on Signals, Systems and Computers. IEEE, 2016. http://dx.doi.org/10.1109/acssc.2016.7869633.

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Lu, Shiyin, Guanghui Wang, Yao Hu, and Lijun Zhang. "Multi-Objective Generalized Linear Bandits." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/427.

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In this paper, we study the multi-objective bandits (MOB) problem, where a learner repeatedly selects one arm to play and then receives a reward vector consisting of multiple objectives. MOB has found many real-world applications as varied as online recommendation and network routing. On the other hand, these applications typically contain contextual information that can guide the learning process which, however, is ignored by most of existing work. To utilize this information, we associate each arm with a context vector and assume the reward follows the generalized linear model (GLM). We adop

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Farooq, Umar, and Brian F. Feeny. "An Experimental Investigation of State-Variable Modal Decomposition Method for Modal Analysis." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-87060.

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This work presents the experimental evaluation of the statevariable modal decomposition (SVMD) method for modal parameter estimation of multi degree-of-freedom (MDOF) vibration systems. Using output response ensembles only, the generalized eigenvalue problem is formed to estimate eigen frequencies and modal vectors for a lightly damped linear clamped-free experimental Euler beam. The estimated frequencies and modal vectors are compared against the theoretical system frequencies and modal vectors. Satisfactory results are obtained for estimating both system frequencies and modal vectors for the

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Yamashita, Hiroki, Antti I. Valkeapää, Paramsothy Jayakumar, and Hiroyuki Sugiyama. "Bi-Linear Shear Deformable ANCF Shell Element Using Continuum Mechanics Approach." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-35349.

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In this investigation, a bi-linear shear deformable shell element is developed using the absolute nodal coordinate formulation for the large deformation analysis of multibody shell structures. The element consists of four nodes, each of which has the global position coordinates and the gradient coordinates along the thickness introduced for describing the orientation and deformation of the cross section of the shell element. The global position field on the mid-plane and the position vector gradient at a material point in the element are interpolated by bi-linear polynomials. The continuum mec

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Zhang, Liping, Delun Wang, and Jian S. Dai. "Automated Conceptual Design for Hybrid Mechanisms Based on Characteristic State Space Theory." In ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/detc2008-49910.

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A new methodology for automated conceptual design of hybrid combined mechanical system based on the characteristic state space approach is developed. The goal is to conceive the appropriate hybrid system given only the kinematic function posed in the problem specifications. The key enabler of the conceptual synthesis is the method of capturing kinematic behavior and transforming the continuous and non-linear motion into the discrete states and linear relation characteristic stack representation. A new characteristic state analyzing method for multi-dof primary mechanism blocks is proposed. The

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Hwang, Jon Li, and Ting Nung Shiau. "Application of Generalized Polynomial Expansion Method to Nonlinear Rotor Bearing Systems." In ASME 1991 Design Technical Conferences. American Society of Mechanical Engineers, 1991. http://dx.doi.org/10.1115/detc1991-0269.

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Abstract The Generalized Polynomial Expansion Method (GPEM) is utilized to model a large-order flexible-rotor system with nonlinear supports. With the application of GPEM, a set of nonlinear ordinary differential equations are obtained. A hybrid method which combines the merits of the Harmonic Balance Method (HBM) and the Trigonometric Collocation Method (TCM) is used to solve for the nonlinear response of the system. This hybrid method together with reduction techniques can efficiently solve for the motion of the system. The overall algorithm presented provides a very efficient technique for

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Sandberg, R. D., R. Tan, J. Weatheritt, et al. "Applying Machine Learnt Explicit Algebraic Stress and Scalar Flux Models to a Fundamental Trailing Edge Slot." In ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/gt2018-75444.

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A form of supervised machine learning was applied to highly resolved large-eddy simulation (LES) data to develop non linear turbulence stress and heat flux closures with increased prediction accuracy for trailing-edge cooling slot cases. The LES data were generated for a thick and a thin trailing-edge slot and shown to agree well with experimental data, thus providing suitable training data for model development. A Gene Expression Programming (GEP) based algorithm was used to symbolically regress novel nonlinear Explicit Algebraic Stress Models (EASM) and heat-flux closures based on either the

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Kyung, Min-Ho, and Elisha Sacks. "Nonlinear Kinematic Tolerance Analysis of Planar Higher Pairs." In ASME 2002 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2002. http://dx.doi.org/10.1115/detc2002/dac-34109.

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This paper presents a nonlinear kinematic tolerance analysis algorithm for higher pairs based on configuration space construction. The input is a pair of planar parts whose profiles consist of line and circle segments. Each part translates along a planar axis or rotates around an orthogonal axis. The part shapes and motion axes are parameterized by a vector of tolerance parameters with range limits. The algorithm constructs a generalized configuration space, called a contact zone, that bounds the worst-case kinematic variation over the tolerance parameter range. The zone specifies the variatio

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Lee, Kuan-Yun, and Thomas A. Courtade. "Linear Models are Most Favorable among Generalized Linear Models." In 2020 IEEE International Symposium on Information Theory (ISIT). IEEE, 2020. http://dx.doi.org/10.1109/isit44484.2020.9174124.

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Jia, Bingyan, Danlin Hou, Liangzhu (Leon) Wang, and Ibrahim Galal Hassan. "Estimation of Room-Level Cooling Energy in Hot/Arid Climate by Machine Learning-Based Approaches." In ASME 2021 Verification and Validation Symposium. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/vvs2021-65272.

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Abstract Building energy models (BEM) are developed for understanding a building’s energy performance. A meta-model of the whole building energy analysis is often used for the BEM calibration and energy prediction. The literature review shows that studies with a focus on the development of room-level meta-models are missing. This study aims to address this research gap through a case study of a residential building with 138 apartments in Doha, Qatar. Five parameters, including cooling setpoint, number of occupants, lighting power density, equipment power density, and interior solar reflectance

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Reports on the topic "Vector generalized linear models"

1

Juricek, Ben C. Generalized Linear Mixed-Effects Models in R. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada413561.

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Carroll, Raymond J. Covariance Analysis in Generalized Linear Measurement Error Models. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada197661.

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Bianchi, Francesco, and Giovanni Nicolò. A Generalized Approach to Indeterminacy in Linear Rational Expectations Models. National Bureau of Economic Research, 2017. http://dx.doi.org/10.3386/w23521.

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Stefanski, Leonard A., and Raymond J. Carroll. Conditional Scores and Optimal Scores for Generalized Linear Measurement-Error Models. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada168533.

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Kunsch, H. R., L. A. Stefanski, and R. J. Carroll. Conditionally Unbiased Bounded Influence Robust Regression with Applications to Generalized Linear Models. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada186319.

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Stefanski, L. A., R. J. Carroll, and D. Ruppert. Optimally Bounded Score Functions for Generalized Linear Models with Applications to Logistic Regression. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada160348.

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Czado, Claudia, and Adrian E. Raftery. Choosing the Link Function and Accounting for Link Uncertainty in Generalized Linear Models using Bayes Factors. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada459482.

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