Relevant bibliographies by topics / Generalized additive models

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

Academic literature on the topic 'Generalized additive models'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 March 2023

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Journal articles on the topic "Generalized additive models"

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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.

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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.

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Hastie, Trevor, and Robert Tibshirani. "Generalized Additive Models." Statistical Science 1, no. 3 (August 1986): 297–310. http://dx.doi.org/10.1214/ss/1177013604.

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De Veaux, Richard D. "Generalized Additive Models." Technometrics 34, no. 2 (May 1992): 225–26. http://dx.doi.org/10.1080/00401706.1992.10484913.

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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.

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Hilbe, Joseph M. "Generalized Additive Models Software." American Statistician 47, no. 1 (February 1993): 59. http://dx.doi.org/10.2307/2684787.

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Chen, Colin. "Generalized additive mixed models." Communications in Statistics - Theory and Methods 29, no. 5-6 (January 2000): 1257–71. http://dx.doi.org/10.1080/03610920008832543.

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Brillinger, David R. "[Generalized Additive Models]: Comment." Statistical Science 1, no. 3 (August 1986): 310–12. http://dx.doi.org/10.1214/ss/1177013605.

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Nelder, J. A. "[Generalized Additive Models]: Comment." Statistical Science 1, no. 3 (August 1986): 312. http://dx.doi.org/10.1214/ss/1177013606.

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Stone, Charles J. "[Generalized Additive Models]: Comment." Statistical Science 1, no. 3 (August 1986): 312–14. http://dx.doi.org/10.1214/ss/1177013607.

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Dissertations / Theses on the topic "Generalized additive models"

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Joshi, Miland. "Applications of generalized additive models." Thesis, University of Warwick, 2011. http://wrap.warwick.ac.uk/47759/.

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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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Pya, Natalya. "Additive models with shape constraints." Thesis, University of Bath, 2010. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.527433.

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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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Utami, Zuliana Sri. "Penalized regression methods with application to generalized linear models, generalized additive models, and smoothing." Thesis, University of Essex, 2017. http://repository.essex.ac.uk/20908/.

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Recently, penalized regression has been used for dealing problems which found in maximum likelihood estimation such as correlated parameters and a large number of predictors. The main issues in this regression is how to select the optimal model. In this thesis, Schall’s algorithm is proposed as an automatic selection of weight of penalty. The algorithm has two steps. First, the coefficient estimates are obtained with an arbitrary penalty weight. Second, an estimate of penalty weight λ can be calculated by the ratio of the variance of error and the variance of coefficient. The iteration is continued from step one until an estimate of penalty weight converge. The computational cost is minimized because the optimal weight of penalty could be obtained within a small number of iterations. In this thesis, Schall’s algorithm is investigated for ridge regression, lasso regression and two-dimensional histogram smoothing. The proposed algorithm are applied to real data sets and simulation data sets. In addition, a new algorithm for lasso regression is proposed. The performance of results of the algorithm was almost comparable in all applications. Schall’s algorithm can be an efficient algorithm for selection of weight of penalty.
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Marra, Giampiero. "Some problems in model specification and inference for generalized additive models." Thesis, University of Bath, 2010. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.527788.

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Regression models describingthe dependence between a univariate response and a set of covariates play a fundamental role in statistics. In the last two decades, a tremendous effort has been made in developing flexible regression techniques such as generalized additive models(GAMs) with the aim of modelling the expected value of a response variable as a sum of smooth unspecified functions of predictors. Many nonparametric regression methodologies exist includinglocal-weighted regressionand smoothing splines. Here the focus is on penalized regression spline methods which can be viewed as a generalization of smoothing splines with a more flexible choice of bases and penalties. This thesis addresses three issues. First, the problem of model misspecification is treated by extending the instrumental variable approach to the GAM context. Second, we study the theoretical and empirical properties of the confidence intervals for the smooth component functions of a GAM. Third, we consider the problem of variable selection within this flexible class of models. All results are supported by theoretical arguments and extensive simulation experiments which shed light on the practical performance of the methods discussed in this thesis.
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Feng, Zhenghui. "Estimation and selection in additive and generalized linear models." HKBU Institutional Repository, 2012. https://repository.hkbu.edu.hk/etd_ra/1435.

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Hercz, Daniel. "Flexible modeling with generalized additive models and generalized linear mixed models: comprehensive simulation and case studies." Thesis, McGill University, 2013. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=114300.

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This thesis compares GAMs and GLMMs in the context of modeling nonlinear curves. The study contains a comprehensive simulation and a few real life data analyses. The simulation uses thousands of generated datasets to compare and contrast the two models' (and linear models as a benchmark) fit, extent of nonlinearity, and shape of the resulting curve. The data analyses extend the results of the simulation to GLMM/GAM curves of lung function with measures of smoking as the independent variable. An additional and larger real life data analysis with dichotomous outcomes rounds out the study and allow for more representative results.
Cette these compare des GAM et GLMM dans le cadre de la modélisation des courbes non-linéaires. L'étude comprend une simulation complète et quelques analyses réelles. La simulation utilise des milliers de 'datasets' générés pour comparer forme entres les deux modèles (et les modèles linéaires comme point de repère), l'étendue de la non-linéarité, et la forme de la courbe obtenue. Les analyses d'étendre les résultats de la simulation à courbes de la fonction pulmonaire avec de GLMM / GAM avec mesures du tabagisme (la variable indépendante). Un autre analyse réelle avec les résultats dichotomiques complète l'étude et que les résultats soient plus représentatifs.
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Koehn, Sebastian. "Generalized additive models in the context of shipping economics." Thesis, University of Leicester, 2009. http://hdl.handle.net/2381/4172.

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This thesis addresses three current issues in maritime economics by the application of semi-parametric estimations within a generalized additive model framework. First, this thesis shows that there are vessel and contract specific differences in time charter rates for dry bulk vessels. The literature on microeconomic factors of time charter rates could show the emergence of a two-tier tanker market during the post-OPA90 period. However, previous results do not allow for any safe conclusions about the existence of a two-tier dry bulk market. This thesis extends the results of previous research by showing that a good part of the variation in physical time charter rates is due to microeconomic factors. It empirically proves the existence of a two-tier dry-bulk market. Controlling for a variety of contract specific effects as well as vessel specific factors the presented model quantifies quality induced differences in physical dry bulk charter rates. Second, the literature on the formation of ship prices focuses exclusively on rather homogeneous shipping segments, such as tankers and dry bulk carriers. Due to the comparatively low number of sales and the complexity of the ships, vessel valuation in highly specialised and small sectors, such as chemical tankers, is a much more challenging task. The empirical results of this thesis confirm the findings in recent literature that ship valuation is a non-linear function of size, age and market conditions, whilst other parameters that are particular to the chemicals market also play a significant role. The third topic addresses the recent increase in operational expenses of merchant vessels (opex). The available literature cannot explain the development nor provides information on vessel individual level. This thesis considers a quantitative model of opex that is particularly successful in explaining the variation in opex across vessels of different type, size, age and specification. The results confirm that differences in opex are due to the behaviour of a vessel's operator and to regulatory requirements. Furthermore, it shows that there are significant differences in opex due to earnings and employment status of a vessel.
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Pan, Yiyang. "A robust fit for generalized partial linear partial additive models." Thesis, University of British Columbia, 2013. http://hdl.handle.net/2429/44647.

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In regression studies, semi-parametric models provide both flexibility and interpretability. In this thesis, we focus on a robust model fitting algorithm for a family of semi-parametric models – the Generalized Partial Linear Partial Addi- tive Models (GAPLMs), which is a hybrid of the widely-used Generalized Linear Models (GLMs) and Generalized Additive Models (GAMs). The traditional model fitting algorithms are mainly based on likelihood proce- dures. However, the resulting fits can be severely distorted by the presence of a small portion of atypical observations (also known as “outliers”), which deviate from the assumed model. Furthermore, the traditional model diag- nostic methods might also fail to detect outliers. In order to systematically solve these problems, we develop a robust model fitting algorithm which is resistant to the effect of outliers. Our method combines the backfitting algorithm and the generalized Speckman estimator to fit the “partial linear partial additive” styled models. Instead of using the likelihood-based weights and adjusted response from the generalized local scoring algorithm (GLSA), we apply the robust weights and adjusted response derived form the robust quasi-likelihood proposed by Cantoni and Ronchetti (2001). We also extend previous methods by proposing a model prediction algorithm for GAPLMs. To compare our robust method with the non-robust one given by the R function gam::gam(), which uses the backfitting algorithm and the GLSA, we report the results of a simulation study. The simulation results show that our robust fit can effectively resist the damage of outliers and it performs similarly to non-robust fit in clean datasets. Moreover, our robust algorithm is observed to be helpful in identifying outliers, by comparing the fitted values with the observed response variable. In the end, we apply our method to analyze the global phytoplankton data. We interpret the outliers reported by our robust fit with an exploratory analysis and we see some interesting patterns of those outliers in the dataset. We believe our result can provide more information for the relative research.
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De, Zan Martina <1994&gt. "ExplainableAI: on explaining forest of decision trees by using generalized additive models." Master's Degree Thesis, Università Ca' Foscari Venezia, 2021. http://hdl.handle.net/10579/18604.

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In recent years, decision support systems have become more and more perva- sive in our society, playing an important role in our everyday life. But these systems, often called black-box models, are extremely complex and it may be impossible to understand or explain how they work in a human interpretable way. This lack of explainability is an issue: ethically because we have to be sure that our system is fair and reasonable; practically because people tend to trust more what they understand. However, substituting black-box model with a more interpretable one in the process of decision making may be impossible: interpretable model may not work as well as the original one or training data may be no longer available. In this thesis we focus on forests of decision trees, which are particular cases of black-box models. If fact, trees are interpretable models, but forests are composed by thousand of trees that cooperate to take decisions, making the final model too complex to comprehend its behavior. In this work we show that Generalized Additive Models (GAMs) can be used to explain forests of decision trees with a good level of accuracy. In fact, GAMs are linear combination of single-features or pair-features mod- els, called shape functions. Since shape functions can be only one- or two- dimensional functions, they can be easily visualized and interpreted by user. At the same time, shape functions can be arbitrarily complex, making GAMs as powerful as other more complex models.
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VITRANO, Angela. "Modelling Spatio-Temporal Elephant Movement Data: a Generalized Additive Mixed Models Framework." Doctoral thesis, Università degli Studi di Palermo, 2014. http://hdl.handle.net/10447/90988.

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This thesis focuses on understanding how environmental factors influence elephant movement and in investigating the spatio-temporal patterns. The thesis analyses movement data of some African elephants (Loxodonta Africana) living in the Kruger National Park and its associated private reserves of South Africa. Due to heterogeneity among elephants, and nonlinear relationships between elephant movement and environmental variables, Generalized Additive Mixed Models (GAMMs) were employed. Results showed delayed effects of rainfall and temperature and particular trends in time and space.
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Books on the topic "Generalized additive models"

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Hastie, Trevor. Generalized additive models. Boca Raton, Fla: Chapman & Hall/CRC, 1999.

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Hastie, Trevor. Generalized additive models. Toronto: University of Toronto, Dept. of Statistics, 1985.

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Hastie, Trevor. Generalized additive models. London: Chapman and Hall, 1990.

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Hastie, Trevor. Generalized additive models: Some applications. Toronto: University of Toronto, Dept. of Statistics, 1985.

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

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Berhane, Kiros. Generalized additive models for longitudinal data. [Toronto, Ont: University of Toronto, Department of Statistics, 1993.

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Hastie, Trevor. Generalized additive models, cubic splines and personalized likelihood. Toronto: University of Toronto, Dept. of Statistics, 1987.

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Kobayashi, Donald R. Evaluation of time-area closures to reduce incidental sea turtle take in the Hawaii-based longline fishery: Generalized Additive Model (GAM) development and retrospective examination. Honolulu, Hawaii: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Pacific Islands Fisheries Science Center, 2005.

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Hastie, T. J. Generalized Additive Models. CRC Press LLC, 2017.

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Wood, Simon N. Generalized Additive Models. Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/9781315370279.

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Book chapters on the topic "Generalized additive models"

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Sperlich, Stefan, and Jiří Zelinka. "Generalized Additive Models." In XploRe® - Application Guide, 171–220. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-57292-0_7.

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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.

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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.

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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.

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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.

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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.

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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.

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Hastie, Trevor, and Robert Tibshirani. "Generalized Additive Models; Some Applications." In Generalized Linear Models, 66–81. New York, NY: Springer US, 1985. http://dx.doi.org/10.1007/978-1-4615-7070-7_8.

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Denuit, Michel, Donatien Hainaut, and Julien Trufin. "Generalized Additive Models (GAMs)." In Springer Actuarial, 253–327. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25820-7_6.

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Baayen, R. Harald, and Maja Linke. "Generalized Additive Mixed Models." In A Practical Handbook of Corpus Linguistics, 563–91. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46216-1_23.

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Conference papers on the topic "Generalized additive models"

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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.

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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.

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Lafferty, John. "Additive models, boosting, and inference for generalized divergences." In the twelfth annual conference. New York, New York, USA: ACM Press, 1999. http://dx.doi.org/10.1145/307400.307422.

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Waal, D. A. de, and J. V. du Toit. "Generalized Additive Models from a Neural Network Perspective." In 2007 Seventh IEEE International Conference on Data Mining - Workshops (ICDM Workshops). IEEE, 2007. http://dx.doi.org/10.1109/icdmw.2007.127.

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Hongye, Song. "Generalized Additive Models of DEA and Generalized Non-Dominated Solution in Multi-objective Programming." In 2010 International Conference on Computational Intelligence and Security (CIS). IEEE, 2010. http://dx.doi.org/10.1109/cis.2010.136.

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Arnold, Denis, Petra Wagner, and R. Harald Baayen. "Using generalized additive models and random forests to model prosodic prominence in German." In Interspeech 2013. ISCA: ISCA, 2013. http://dx.doi.org/10.21437/interspeech.2013-82.

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Ploennigs, Joern, Bei Chen, Anika Schumann, and Niall Brady. "Exploiting Generalized Additive Models for Diagnosing Abnormal Energy Use in Buildings." In the 5th ACM Workshop. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2528282.2528291.

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Lei An, Hongyu Kang, Yi Xin, Xiaoming Hu, Qin Li, Yin Ling, and Heng Gu. "Generalized additive models of hospital admissions with respiratory disease and meteorology." In 2014 IEEE 3rd International Conference on Cloud Computing and Intelligence Systems (CCIS). IEEE, 2014. http://dx.doi.org/10.1109/ccis.2014.7175750.

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Suhaimi, Nor Hanisah, and Shariffah Suhaila. "Generalized additive models (GAMs) approach in modeling rainfall data over Johor area." In ADVANCES IN INDUSTRIAL AND APPLIED MATHEMATICS: Proceedings of 23rd Malaysian National Symposium of Mathematical Sciences (SKSM23). Author(s), 2016. http://dx.doi.org/10.1063/1.4954626.

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Dineva, Adrienn, and Istvan Vajda. "Insulation State Assessment of Rotating Electrical Machines by Employing Generalized Additive Models." In 2020 XI International Conference on Electrical Power Drive Systems (ICEPDS). IEEE, 2020. http://dx.doi.org/10.1109/icepds47235.2020.9249328.

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Reports on the topic "Generalized additive models"

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Krainyk, Yaroslav M., Anzhela P. Boiko, Dmytro A. Poltavskyi, and Vladimir I. Zaselskiy. Augmented Reality-based historical guide for classes and tourists. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3747.

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In this paper, development of historical guide based on Augmented Reality (AR) technology is considered. The developed guide application it targeted to be used in different scenarios, in particular, during history learning classes, for guidance of the tourists to exhibits both indoor and outdoor. Common features of all these scenarios are generalized and according to them main information and objects model for forming scene are identified. This part is followed by detailed description of objects and scene representation, markers usage, employment of additional services, etc. Finally, the developed historical guide application has been introduced. It harnesses A-Frame library for processing of models and their representation. The application is able to work with different markers so that it can be extended easily. In addition, one of the main benefits of the developed application is support of multiple platforms because it works from web-browser and does not require installation of additional software. The developed application can be effectively used for all provided scenarios and has potential for further extension.
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Lubowa, Nasser, Zita Ekeocha, Stephen Robert Byrn, and Kari L. Clase. Pharmaceutical Industry in Uganda: A Review of the Common GMP Non-conformances during Regulatory Inspections. Purdue University, December 2021. http://dx.doi.org/10.5703/1288284317442.

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The prevalence of substandard medicines in Africa is high but not well documented. Low and Middle-Income Countries (LMICs) are likely to face considerable challenges with substandard medications. Africa faces inadequate drug regulatory practices, and in general, compliance with Good Manufacturing Practices (GMP) in most of the pharmaceutical industries is lacking. The majority of pharmaceutical manufacturers in developing countries are often overwhelmed by the GMP requirements and therefore are unable to operate in line with internationally acceptable standards. Non-conformances observed during regulatory inspections provide the status of the compliance to GMP requirements. The study aimed to identify the GMP non-conformances during regulatory inspections and gaps in the production of pharmaceuticals locally manufactured in Uganda by review of the available 50 GMP reports of 21 local pharmaceutical companies in Uganda from 2016. The binary logistic generalized estimating equations (GEE) model was applied to estimate the association between odds of a company failing to comply with the GMP requirements and non-conformances under each GMP inspection parameter. Analysis using dummy estimation to linear regression included determination of the relationship that existed between the selected variables (GMP inspection parameters) and the production capacity of the local pharmaceutical industry. Oral liquids, external liquid preparations, powders, creams, and ointments were the main categories of products manufactured locally. The results indicated that 86% of the non-conformances were major, 11% were minor, and 3% critical. The majority of the non-conformances were related to production (30.1%), documentation (24.5%), and quality control (17.6%). Regression results indicated that for every non-conformance under premises, equipment, and utilities, there was a 7-fold likelihood of the manufacturer failing to comply with the GMP standards (aOR=6.81, P=0.001). The results showed that major non-conformances were significantly higher in industries of small scale (B=6.77, P=0.02) and medium scale (B=8.40, P=0.04), as compared to those of large scale. This study highlights the failures in quality assurance systems and stagnated GMP improvements in these industries that need to be addressed by the manufacturers with support from the regulator. The addition of risk assessment to critical production and quality control operations and establishment of appropriate corrective and preventive actions as part of quality management systems are required to ensure that quality pharmaceuticals are manufactured locally.
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You might also be interested in the extended bibliographies on the topic 'Generalized additive models' for particular source types:
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