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Relevant bibliographies by topics / Scalar-On-Function

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Journal articles

Academic literature on the topic 'Scalar-On-Function'

Author: Grafiati

Published: 18 May 2024

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Journal articles on the topic "Scalar-On-Function"

1

Reiss, Philip T., Jeff Goldsmith, Han Lin Shang, and R. Todd Ogden. "Methods for Scalar-on-Function Regression." International Statistical Review 85, no. 2 (2016): 228–49. http://dx.doi.org/10.1111/insr.12163.

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2

Fan, Zhaohu, and Matthew Reimherr. "High-dimensional adaptive function-on-scalar regression." Econometrics and Statistics 1 (January 2017): 167–83. http://dx.doi.org/10.1016/j.ecosta.2016.08.001.

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3

Chen, Yakuan, Jeff Goldsmith, and R. Todd Ogden. "Variable selection in function-on-scalar regression." Stat 5, no. 1 (2016): 88–101. http://dx.doi.org/10.1002/sta4.106.

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4

Guo, X., J. Hua, and H. Qin. "Scalar-function-driven editing on point set surfaces." IEEE Computer Graphics and Applications 24, no. 4 (2004): 43–52. http://dx.doi.org/10.1109/mcg.2004.16.

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5

Bauer, Alexander, Fabian Scheipl, Helmut Küchenhoff, and Alice-Agnes Gabriel. "An introduction to semiparametric function-on-scalar regression." Statistical Modelling 18, no. 3-4 (2018): 346–64. http://dx.doi.org/10.1177/1471082x17748034.

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Abstract:

Abstract: Function-on-scalar regression models feature a function over some domain as the response while the regressors are scalars. Collections of time series as well as 2D or 3D images can be considered as functional responses. We provide a hands-on introduction for a flexible semiparametric approach for function-on-scalar regression, using spatially referenced time series of ground velocity measurements from large-scale simulated earthquake data as a running example. We discuss important practical considerations and challenges in the modelling process and outline best practices. The outline of our approach is complemented by comprehensive R code, freely available in the online appendix. This text is aimed at analysts with a working knowledge of generalized regression models and penalized splines.

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6

Wang, Xu, Jiaqing Kou, and Weiwei Zhang. "Unsteady aerodynamic modeling based on fuzzy scalar radial basis function neural networks." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 233, no. 14 (2019): 5107–21. http://dx.doi.org/10.1177/0954410019836906.

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In this paper, a fuzzy scalar radial basis function neural network is proposed, in order to overcome the limitation of traditional aerodynamic reduced-order models having difficulty in adapting to input variables with different orders of magnitude. This network is a combination of fuzzy rules and standard radial basis function neural network, and all the basis functions are defined as scalar basis functions. The use of scalar basis function will increase the flexibility of the model, thus enhancing the generalization capability on complex dynamic behaviors. Particle swarm optimization algorithm is used to find the optimal width of the scalar basis function. The constructed reduced-order models are used to model the unsteady aerodynamics of an airfoil in transonic flow. Results indicate that the proposed reduced-order models can capture the dynamic characteristics of lift coefficients at different reduced frequencies and amplitudes very accurately. Compared with the conventional reduced-order model based on recursive radial basis function neural network, the fuzzy scalar radial basis function neural network shows better generalization capability for different test cases with multiple normalization methods.

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7

Reiss, Philip T., David L. Miller, Pei-Shien Wu, and Wen-Yu Hua. "Penalized Nonparametric Scalar-on-Function Regression via Principal Coordinates." Journal of Computational and Graphical Statistics 26, no. 3 (2017): 569–78. http://dx.doi.org/10.1080/10618600.2016.1217227.

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8

Goldsmith, Jeff, and Fabian Scheipl. "Estimator selection and combination in scalar-on-function regression." Computational Statistics & Data Analysis 70 (February 2014): 362–72. http://dx.doi.org/10.1016/j.csda.2013.10.009.

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9

Ciarleglio, Adam, and R. Todd Ogden. "Wavelet-based scalar-on-function finite mixture regression models." Computational Statistics & Data Analysis 93 (January 2016): 86–96. http://dx.doi.org/10.1016/j.csda.2014.11.017.

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10

Yang, Hojin, Veerabhadran Baladandayuthapani, Arvind U. K. Rao, and Jeffrey S. Morris. "Quantile Function on Scalar Regression Analysis for Distributional Data." Journal of the American Statistical Association 115, no. 529 (2019): 90–106. http://dx.doi.org/10.1080/01621459.2019.1609969.

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