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Koyak, Robert A., T. J. Hastie, and R. J. Tibshirani. "Generalized Additive Models." Journal of the American Statistical Association 86, no. 416 (December 1991): 1140. http://dx.doi.org/10.2307/2290538.
Brown, R. A., T. J. Hastie, and R. J. Tibshirani. "Generalized Additive Models." Biometrics 47, no. 2 (June 1991): 785. http://dx.doi.org/10.2307/2532174.
Ravikumar, Pradeep, John Lafferty, Han Liu, and Larry Wasserman. "Sparse additive models." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 71, no. 5 (November 2009): 1009–30. http://dx.doi.org/10.1111/j.1467-9868.2009.00718.x.
Müller, Hans-Georg, and Fang Yao. "Functional Additive Models." Journal of the American Statistical Association 103, no. 484 (December 2008): 1534–44. http://dx.doi.org/10.1198/016214508000000751.
Bacchetti, Peter. "Additive Isotonic Models." Journal of the American Statistical Association 84, no. 405 (March 1989): 289–94. http://dx.doi.org/10.1080/01621459.1989.10478768.
Moon, Graham, T. J. Hastie, and R. J. Tibshirani. "Generalized Additive Models." Applied Statistics 41, no. 1 (1992): 219. http://dx.doi.org/10.2307/2347636.
In many practical situations when analyzing a dependence of one or more explanatory variables on a response variable it is essential to assume that the relationship of interest obeys certain shape constraints, such as monotonicity or monotonicity and convexity/concavity. In this thesis a new approach to shape preserving smoothing within generalized additive models has been developed. In contrast with previous quadratic programming based methods, the project develops intermediate rank penalized smoothers with shape constrained restrictions based on re-parameterized B-splines and penalties based on the P-spline ideas of Eilers and Marx (1996). Smoothing under monotonicity constraints and monotonicity together with convexity/concavity for univariate smooths; and smoothing of bivariate functions with monotonicity restrictions on both covariates and on only one of them are considered. The proposed shape constrained smoothing has been incorporated into generalized additive models with a mixture of unconstrained and shape restricted smooth terms (mono-GAM). A fitting procedure for mono-GAM is developed. Since a major challenge of any flexible regression method is its implementation in a computationally efficient and stable manner, issues such as convergence, rank deficiency of the working model matrix, initialization, and others have been thoroughly dealt with. A question about the limiting posterior distribution of the model parameters is solved, which allows us to construct Bayesian confidence intervals of the mono-GAM smooth terms by means of the delta method. The performance of these confidence intervals is examined by assessing realized coverage probabilities using simulation studies. The proposed modelling approach has been implemented in an R package monogam. The model setup is the same as in mgcv(gam) with the addition of shape constrained smooths. In order to be consistent with the unconstrained GAM, the package provides key functions similar to those associated with mgcv(gam). Performance and timing comparisons of mono-GAM with other alternative methods has been undertaken. The simulation studies show that the new method has practical advantages over the alternatives considered. Applications of mono-GAM to various data sets are presented which demonstrate its ability to model many practical situations.
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Joshi, Miland. "Applications of generalized additive models." Thesis, University of Warwick, 2011. http://wrap.warwick.ac.uk/47759/.
Main Purpose The study is primarily a contribution to a question of strategy rather than the development of a new method. It explores the circumstances in which the use of generalized additive models can be recommended. It is thus a contribution to answering the question "When is it a good idea (or not so good an idea) to use GAMs?" Content Following an introductory exposition in which they are compared to generalized linear models, subsequent chapters deal with evidence that could support possible recommendations: 1. A survey of recent studies, in which GAMs have been used and recommended, regarded with greater reserve, or compared to other methods. 2. Original case studies in which the applicability of GAMs is investigated, namely: (a) Receiver operating characteristic curves used in medical diagnostic testing, the associated diagnostic likelihood ratios, and the modelling of the risk score. (b) A study of a possible heat wave effect on mortality in London. (c) Shorter studies, including a study of factors influencing the length of stay in hospital in Queensland, Australia, and a simulation study. 3. Diagnostics, looking in particular at concurvity, and the problems of defining and detecting it. The study ends with recommendations for the use of GAMs, and possible areas for further research. The appendices include a glossary, technical appendices and R code for computations involved in the project.
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Piccirilli, Marco. "Additive models for energy markets." Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3426712.
This Dissertation explores the capability of additive models to describe prices in energy
markets, by focusing in particular on the specific case of electricity and natural gas. In
Chapter 1 we study a dynamic portfolio optimization problem designed for intraday electricity
trading. In Chapter 2 we introduce a no-arbitrage tractable framework based on the Heath-
Jarrow-Morton approach for a multicommodity energy forward market. Chapter 3 deals with
a thorough empirical study of a two-factor model derived by the framework of Chapter 2,
with an application to the German power futures market. Finally, in Chapter 4 we discuss
option pricing for additive factor models by Fourier transform methods. We introduce a
two-factor futures price model with jumps in order to capture the implied volatility smile
of European electricity options. An application to the European Energy Exchange Power
Derivatives market is presented. Questa Tesi esplora la capacità dei modelli additivi di descrivere i prezzi nei mercati energetici,
concentrandosi in particolare sul caso specifico dell’elettricità e del gas naturale. Nel Capitolo
1 studiamo un problema di ottimizzazione dinamica di portafoglio per il trading di energia
elettrica su mercati intraday. Nel Capitolo 2 introduciamo un framework trattabile e privo di
arbitraggio basato sull’approccio di Heath-Jarrow-Morton per mercati a termine energetici
multicommodity. Il Capitolo 3 si occupa di uno studio empirico approfondito di un modello a
due fattori derivato dal framework del Capitolo 2, con un’applicazione al mercato a termine
elettrico tedesco. Infine, nel Capitolo 4 discutiamo il prezzaggio di opzioni per modelli fattoriali
additivi con metodi di trasformata di Fourier. Introduciamo un modello di prezzi futures a
due fattori con salti al fine di catturare lo smile delle volatilità implicite di opzioni Europee
sull’elettricità. Viene presentata un’applicazione al mercato European Energy Exchange Power
Derivatives.
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譚維新 and Wai-san Wilson Tam. "Implementation and applications of additive models." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1999. http://hub.hku.hk/bib/B31221671.
Tam, Wai-san Wilson. "Implementation and applications of additive models /." Hong Kong : University of Hong Kong, 1999. http://sunzi.lib.hku.hk/hkuto/record.jsp?B20715444.
High-dimensional data offers researchers increased ability to find useful factors in predicting a response. However, determination of the most important factors requires careful selection of the explanatory variables. In order to tackle this challenge, much work has been done on single or grouped variable selection under the penalized regression framework. Although the topic of variable selection has been extensively studied under the parametric framework, its applications to more flexible nonparametric models are yet to be explored.
In order to implement the variable selection in nonparametric additive models, I introduce and study two nonconvex selection methods under the penalized regression framework, namely the group MCP and the adaptive group LASSO, aiming at improvements on the selection performances of the more widely known group LASSO method in such models. One major part of the dissertation focuses on the theoretical properties of the group MCP and the adaptive group LASSO. I derive their selection and estimation properties. The application of the presently proposed methods to nonparametric additive models are further examined using simulation. Their applications to areas such as the economics and genomics are presented as well. Under both the simulation studies and data applications, the group MCP and the adaptive group LASSO have shown their advantages over the more traditionally used group LASSO method.
For the proposed adaptive group LASSO that uses the newly proposed weights, whose recursive application is therefore never studied before, I also derive its theoretical properties under a very general framework. Simulation studies under linear regression are included.
In addition to the theoretical and empirical investigations, throughout the dissertation, several other important issues have been briefly discussed, including the computing algorithms and different ways of selecting tuning parameters.
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Läuter, Henning. "Estimation in partly parametric additive Cox models." Universität Potsdam, 2003. http://opus.kobv.de/ubp/volltexte/2011/5150/.
The dependence between survival times and covariates is described e.g. by proportional hazard models. We consider partly parametric Cox models and discuss here the estimation of interesting parameters. We represent the ma- ximum likelihood approach and extend the results of Huang (1999) from linear to nonlinear parameters. Then we investigate the least squares esti- mation and formulate conditions for the a.s. boundedness and consistency of these estimators.
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Hofner, Benjamin [Verfasser]. "Boosting in Structured Additive Models / Benjamin Hofner." München : Verlag Dr. Hut, 2012. http://d-nb.info/1020299223/34.
Schimek, Michael G., and Berwin A. Turlach. "Additive and Generalized Additive Models." In Smoothing and Regression, 277–327. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118150658.ch10.
West, Robert M. "Generalised Additive Models." In Modern Methods for Epidemiology, 261–78. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-3024-3_15.
Everitt, Brian, and Sophia Rabe-Hesketh. "Generalized Additive Models." In Analyzing Medical Data Using S-PLUS, 291–303. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3285-6_14.
Ohlsson, Esbjörn, and Björn Johansson. "Generalized Additive Models." In EAA Lecture Notes, 101–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-10791-7_5.
Härdle, Wolfgang, Axel Werwatz, Marlene Müller, and Stefan Sperlich. "Generalized Additive Models." In Springer Series in Statistics, 253–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17146-8_9.
Harezlak, Jaroslaw, David Ruppert, and Matt P. Wand. "Generalized Additive Models." In Use R!, 71–128. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-8853-2_3.
von Auer, Ludwig. "Additive Utility Models." In Lecture Notes in Economics and Mathematical Systems, 47–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-58879-2_4.
Higdon, Roger. "Generalized Additive Models." In Encyclopedia of Systems Biology, 814–15. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_1197.
Klein, John P., and Melvin L. Moeschberger. "Additive Hazards Regression Models." In Statistics for Biology and Health, 305–26. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4757-2728-9_10.
Carvalho, Francisco, and Ricardo Covas. "Mixed additive models." In INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2015 (ICNAAM 2015). Author(s), 2016. http://dx.doi.org/10.1063/1.4951831.
Yamada, Yuji. "Optimal Hedging with Additive Models." In Proceedings of the KIER–TMU International Workshop on Financial Engineering 2010. WORLD SCIENTIFIC, 2011. http://dx.doi.org/10.1142/9789814366038_0011.
Zhang, Xuezhou, Sarah Tan, Paul Koch, Yin Lou, Urszula Chajewska, and Rich Caruana. "Axiomatic Interpretability for Multiclass Additive Models." In KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3292500.3330898.
Zhuang, Honglei, Xuanhui Wang, Michael Bendersky, Alexander Grushetsky, Yonghui Wu, Petr Mitrichev, Ethan Sterling, Nathan Bell, Walker Ravina, and Hai Qian. "Interpretable Ranking with Generalized Additive Models." In WSDM '21: The Fourteenth ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3437963.3441796.
Williams, Simon, and Murk J. Bottema. "Additive Dirichlet models for projectional images." In SPIE Medical Imaging, edited by David R. Haynor and Sébastien Ourselin. SPIE, 2012. http://dx.doi.org/10.1117/12.911862.
Sulej, Wojciech, and Krzysztof Murawski. "Application of 3D printed models to visual measurement in the new innovative depth from defocus method." In 3D Printed Optics and Additive Photonic Manufacturing, edited by Georg von Freymann, Alois M. Herkommer, and Manuel Flury. SPIE, 2018. http://dx.doi.org/10.1117/12.2306955.
Kormaksson, Matthias, Luciano Barbosa, Marcos R. Vieira, and Bianca Zadrozny. "Bus Travel Time Predictions Using Additive Models." In 2014 IEEE International Conference on Data Mining (ICDM). IEEE, 2014. http://dx.doi.org/10.1109/icdm.2014.107.
Semenovich, Dimitri, Nobuyuki Morioka, and Arcot Sowmya. "Efficient Additive Models via the Generalized Lasso." In 2010 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2010. http://dx.doi.org/10.1109/icdmw.2010.184.
Tresadern, Philip, Patrick Sauer, and Tim Cootes. "Additive Update Predictors in Active Appearance Models." In British Machine Vision Conference 2010. British Machine Vision Association, 2010. http://dx.doi.org/10.5244/c.24.91.
Thakkar, Ajit J. "A hierarchy for additive models of polarizability." In INTERNATIONAL CONFERENCE OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2009: (ICCMSE 2009). AIP, 2012. http://dx.doi.org/10.1063/1.4771764.
Koenker, Roger. Additive models for quantile regression: model selection and confidence bandaids. Institute for Fiscal Studies, November 2010. http://dx.doi.org/10.1920/wp.cem.2010.3310.
Haslam, J., J. Kelly, and R. Harris. Predictive Models for Ceramic Additive Manufacturing, CRADA No. TC02251. Office of Scientific and Technical Information (OSTI), April 2022. http://dx.doi.org/10.2172/1863168.
McKeague, Ian W., and Klaus J. Utikal. Goodness-of-Fit Tests for Additive Hazards and Proportional Hazards Models. Fort Belvoir, VA: Defense Technical Information Center, October 1988. http://dx.doi.org/10.21236/ada202440.
Haslam, Jeff, James Kelly, and Randy Harris. Predicitve Models for Ceramic Additive Manufacturing, Final Report CRADA No. TC02251. Office of Scientific and Technical Information (OSTI), April 2019. http://dx.doi.org/10.2172/1525465.
Dong, Chaohua, and Oliver Linton. Additive nonparametric models with time variable and both stationary and nonstationary regressions. The IFS, December 2017. http://dx.doi.org/10.1920/wp.cem.2017.5917.
Allen, Jeffrey, Robert Moser, Zackery McClelland, Md Mohaiminul Islam, and Ling Liu. Phase-field modeling of nonequilibrium solidification processes in additive manufacturing. Engineer Research and Development Center (U.S.), December 2021. http://dx.doi.org/10.21079/11681/42605.
This project models dendrite growth during nonequilibrium solidification of binary alloys using the phase-field method (PFM). Understanding the dendrite formation processes is important because the microstructural features directly influence mechanical properties of the produced parts. An improved understanding of dendrite formation may inform design protocols to achieve optimized process parameters for controlled microstructures and enhanced properties of materials. To this end, this work implements a phase-field model to simulate directional solidification of binary alloys. For applications involving strong nonequilibrium effects, a modified antitrapping current model is incorporated to help eject solute into the liquid phase based on experimentally calibrated, velocity-dependent partitioning coefficient. Investigated allow systems include SCN, Si-As, and Ni-Nb. The SCN alloy is chosen to verify the computational method, and the other two are selected for a parametric study due to their different diffusion properties. The modified antitrapping current model is compared with the classical model in terms of predicted dendrite profiles, tip undercooling, and tip velocity. Solidification parameters—the cooling rate and the strength of anisotropy—are studied to reveal their influences on dendrite growth. Computational results demonstrate effectiveness of the PFM and the modified antitrapping current model in simulating rapid solidification with strong nonequilibrium at the interface.
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Hauzenberger, Niko, Florian Huber, Gary Koop, and James Mitchell. Bayesian modeling of time-varying parameters using regression trees. Federal Reserve Bank of Cleveland, January 2023. http://dx.doi.org/10.26509/frbc-wp-202305.
In light of widespread evidence of parameter instability in macroeconomic models, many time-varying parameter (TVP) models have been proposed. This paper proposes a nonparametric TVP-VAR model using Bayesian additive regression trees (BART). The novelty of this model stems from the fact that the law of motion driving the parameters is treated nonparametrically. This leads to great flexibility in the nature and extent of parameter change, both in the conditional mean and in the conditional variance. In contrast to other nonparametric and machine learning methods that are black box, inference using our model is straightforward because, in treating the parameters rather than the variables nonparametrically, the model remains conditionally linear in the mean. Parsimony is achieved through adopting nonparametric factor structures and use of shrinkage priors. In an application to US macroeconomic data, we illustrate the use of our model in tracking both the evolving nature of the Phillips curve and how the effects of business cycle shocks on inflationary measures vary nonlinearly with movements in uncertainty.
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McKeague, Ian W., and Peter D. Sasieni. A Partly Parametric Additive Risk Model. Fort Belvoir, VA: Defense Technical Information Center, January 1993. http://dx.doi.org/10.21236/ada260395.
Candy, J. Model Reference Adaptive Control (MRAC) for Additive Manufacturing. Office of Scientific and Technical Information (OSTI), June 2021. http://dx.doi.org/10.2172/1798434.
