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Journal articles on the topic 'Non-linear models'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Potocký, Rastislav, and Van Ban To. "On parameter-effects arrays in non-linear regression models." Applications of Mathematics 38, no. 2 (1993): 123–32. http://dx.doi.org/10.21136/am.1993.104539.

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2

Gasparrini, A., B. Armstrong, and M. G. Kenward. "Distributed lag non-linear models." Statistics in Medicine 29, no. 21 (2010): 2224–34. http://dx.doi.org/10.1002/sim.3940.

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3

Burnett, Richard T., W. H. Ross, and Daniel Krewski. "Non-linear mixed regression models." Environmetrics 6, no. 1 (1995): 85–99. http://dx.doi.org/10.1002/env.3170060108.

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4

Chaokui, Li, Zhu Qing, and Song Chengfang. "Processing approach of non-linear adjustment models in the space of non-linear models." Geo-spatial Information Science 6, no. 2 (2003): 25–30. http://dx.doi.org/10.1007/bf02826750.

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5

Morozumi, T., and S. Nojiri. "An Analysis of Non-Compact Non-Linear Models." Progress of Theoretical Physics 75, no. 3 (1986): 677–85. http://dx.doi.org/10.1143/ptp.75.677.

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6

V. Vallinayagam, V. Vallinayagam, S. Prathap S. Prathap, and P. Venkatesan P. Venkatesan. "Non-Linear Regression Models for Heart Attack Data – An Empirical Comparison." Indian Journal of Applied Research 4, no. 6 (2011): 332–34. http://dx.doi.org/10.15373/2249555x/june2014/102.

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7

Hines, W. G. S., and John Haigh. "Non-linear ESS models and polymorphism." Journal of Applied Probability 22, no. 4 (1985): 747–56. http://dx.doi.org/10.2307/3213942.

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Extensions of ESS theory to situations well outside the classical formulation often assume, as a convenience, that the population being modelled is, in some sense, monomorphic. While this assumption is in keeping with the original approach used in developing the theory, it is rendered less plausible by the observation that the original models do not preclude the possibility of polymorphism, a potentially serious omission. We consider a generalisation of the classical bilinear fitness function, and examine the circumstances that will tend to favour monomorphism.

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8

Baptista, J. M. "Twisting gauged non-linear sigma-models." Journal of High Energy Physics 2008, no. 02 (2008): 096. http://dx.doi.org/10.1088/1126-6708/2008/02/096.

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9

Müller, Peter, Mike West, and Steven MacEachern. "Bayesian Models for Non‐linear Autoregressions." Journal of Time Series Analysis 18, no. 6 (1997): 593–614. http://dx.doi.org/10.1111/1467-9892.00070.

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10

Hines, W. G. S., and John Haigh. "Non-linear ESS models and polymorphism." Journal of Applied Probability 22, no. 04 (1985): 747–56. http://dx.doi.org/10.1017/s0021900200107983.

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Extensions of ESS theory to situations well outside the classical formulation often assume, as a convenience, that the population being modelled is, in some sense, monomorphic. While this assumption is in keeping with the original approach used in developing the theory, it is rendered less plausible by the observation that the original models do not preclude the possibility of polymorphism, a potentially serious omission. We consider a generalisation of the classical bilinear fitness function, and examine the circumstances that will tend to favour monomorphism.

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11

Mhalla, Linda, Valérie Chavez-Demoulin, and Philippe Naveau. "Non-linear models for extremal dependence." Journal of Multivariate Analysis 159 (July 2017): 49–66. http://dx.doi.org/10.1016/j.jmva.2017.04.006.

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12

Abdalla, Elcio, and Michael Forger. "Integrable non-linear ? models with fermions." Communications in Mathematical Physics 104, no. 1 (1986): 123–50. http://dx.doi.org/10.1007/bf01210796.

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13

Wen, Jia-ru, та Han-qing Zheng. "Wormholes and Non-Linear σ Models". Communications in Theoretical Physics 13, № 2 (1990): 267–70. http://dx.doi.org/10.1088/0253-6102/13/2/267.

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14

Cheng, R. C. H., B. E. Evans, and T. C. Iles. "Embedded Models in Non-Linear Regression." Journal of the Royal Statistical Society: Series B (Methodological) 54, no. 3 (1992): 877–88. http://dx.doi.org/10.1111/j.2517-6161.1992.tb01459.x.

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15

Spector, Donald. "Integrability of non-linear sigma models." Physics Letters B 171, no. 2-3 (1986): 231–34. http://dx.doi.org/10.1016/0370-2693(86)91538-8.

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16

Escamilla-Rivera, Celia, Luciano Casarini, Júlio C. Fabris, and Jailson S. Alcaniz. "Linear and non-linear perturbations in dark energy models." Journal of Cosmology and Astroparticle Physics 2016, no. 11 (2016): 010. http://dx.doi.org/10.1088/1475-7516/2016/11/010.

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17

Büchel, C. "211. Linear and non-linear models of brain interactions." Biological Psychiatry 47, no. 8 (2000): S64. http://dx.doi.org/10.1016/s0006-3223(00)00475-3.

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18

Dbouk, Wassim, and Ibrahim Jamali. "Predicting daily oil prices: Linear and non-linear models." Research in International Business and Finance 46 (December 2018): 149–65. http://dx.doi.org/10.1016/j.ribaf.2018.01.003.

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19

Zuk, V., Y. Selin, and I. Shubenkova. "NONLINEAR NONSTATIONARY PROCESSES OF DIFFERENT NATURE. CLASSIFICATION." Znanstvena misel journal, no. 72 (November 21, 2022): 11–15. https://doi.org/10.5281/zenodo.7340679.

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The article is devoted to an actual problem - the development of a classification of non-linear non-stationary processes of different nature. The object of research is non-linear non-stationary processes in ecology, economics and finance. The necessity of developing a classification of non-linear non-stationary physical processes of various nature as a stage of increasing the adequacy of mathematical models of non-linear non-stationary processes and improving the quality of predictive estimates that are calculated using existing models is substantiated. The scheme of the developed classificati

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20

Sharifi, Alireza, Yagob Dinpashoh, and Rasoul Mirabbasi. "Daily runoff prediction using the linear and non-linear models." Water Science and Technology 76, no. 4 (2017): 793–805. http://dx.doi.org/10.2166/wst.2017.234.

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Runoff prediction, as a nonlinear and complex process, is essential for designing canals, water management and planning, flood control and predicting soil erosion. There are a number of techniques for runoff prediction based on the hydro-meteorological and geomorphological variables. In recent years, several soft computing techniques have been developed to predict runoff. There are some challenging issues in runoff modeling including the selection of appropriate inputs and determination of the optimum length of training and testing data sets. In this study, the gamma test (GT), forward selecti

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21

Grammatikopoulou, A., H. D. St John, and D. M. Potts. "Non-linear and linear models in design of retaining walls." Proceedings of the Institution of Civil Engineers - Geotechnical Engineering 161, no. 6 (2008): 311–23. http://dx.doi.org/10.1680/geng.2008.161.6.311.

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22

Gasparrini, Antonio. "Distributed Lag Linear And Non-Linear Models With Penalized Splines." ISEE Conference Abstracts 2015, no. 1 (2015): 3069. http://dx.doi.org/10.1289/isee.2015.2015-3069.

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23

Currie, Iain D. "On fitting generalized linear and non-linear models of mortality." Scandinavian Actuarial Journal 2016, no. 4 (2014): 356–83. http://dx.doi.org/10.1080/03461238.2014.928230.

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24

Renshaw, A. E. "Actuarial graduation practice and generalised linear and non-linear models." Journal of the Institute of Actuaries 118, no. 2 (1991): 295–312. http://dx.doi.org/10.1017/s0020268100019454.

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ABSTRACTIt is demonstrated how existing actuarial graduation practice, used in the construction of life tables, can be extended to considerable effect by formulating the techniques within the generalised linear and non-linear modelling framework.

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25

Achcar, Jorge A., and Sílvia R. C. Lopes. "Linear and Non-Linear Regression Models Assuming a Stable Distribution." Revista Colombiana de Estadística 39, no. 1 (2016): 109–28. http://dx.doi.org/10.15446/rce.v39n1.55144.

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<p>In this paper, we present some computational aspects for a Bayesian analysis involving stable distributions. It is well known that, in general, there is no closed form for the probability density function of a stable distribution. However, the use of a latent or auxiliary random variable facilitates obtaining any posterior distribution when related to stable distributions. To show the usefulness of the computational aspects, the methodology is applied to linear and non-linear regression models. Posterior summaries of interest are obtained using the OpenBUGS software.</p>

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26

Van Pelt, Tobin H., and Dennis S. Bernstein. "Non-linear system identification using Hammerstein and non-linear feedback models with piecewise linear static maps." International Journal of Control 74, no. 18 (2001): 1807–23. http://dx.doi.org/10.1080/00207170110089798.

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27

Takamizawa, Hideyuki, and Isao Shoji. "Approximation of Non-Linear Term Structure Models." Journal of Derivatives 8, no. 3 (2001): 44–51. http://dx.doi.org/10.3905/jod.2001.319156.

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28

Bazilevskiy, Mikhail Pavlovich. "INTERPRETATION OF NON-ELEMENTARY LINEAR REGRESSION MODELS." Information Technology and Mathematical Modeling in the Management of Complex Systems, no. 1 (2022): 5–15. http://dx.doi.org/10.26731/2658-3704.2022.1(13).5-15.

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29

Saayman, Andrea, and Ilsé Botha. "Non-linear models for tourism demand forecasting." Tourism Economics 23, no. 3 (2015): 594–613. http://dx.doi.org/10.5367/te.2015.0532.

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30

Realdon, Marco. "Non-linear Gaussian sovereign CDS pricing models." Quantitative Finance 19, no. 2 (2018): 191–210. http://dx.doi.org/10.1080/14697688.2018.1459808.

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31

De Gooijer, Jan G., and Kurt Brännäs. "Invertibility of non-linear time series models." Communications in Statistics - Theory and Methods 24, no. 11 (1995): 2701–14. http://dx.doi.org/10.1080/03610929508831644.

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32

Huang, Jianhua Z., and Lijian Yang. "Identification of non-linear additive autoregressive models." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 66, no. 2 (2004): 463–77. http://dx.doi.org/10.1111/j.1369-7412.2004.05500.x.

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33

Nouman, Shakeel. "Non-linear growth models for Beetal goats." International Journal of Livestock Production 4, no. 5 (2013): 78–81. http://dx.doi.org/10.5897/ijlp12.030.

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34

Filisetti, A., R. Serra, T. Carletti, M. Villani, and I. Poli. "Non-linear protocell models: synchronization and chaos." European Physical Journal B 77, no. 2 (2010): 249–56. http://dx.doi.org/10.1140/epjb/e2010-00175-5.

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35

BRAUER, F., and A. C. SOUDACK. "Mutualism models with non-linear growth rates†." International Journal of Control 41, no. 6 (1985): 1601–12. http://dx.doi.org/10.1080/0020718508961218.

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36

Baudille, Riccardo, Marco Evangelos Biancolini, and Ernesto Mottola. "Non-linear models of reed valve dynamics." International Journal of Vehicle Systems Modelling and Testing 4, no. 3 (2009): 150. http://dx.doi.org/10.1504/ijvsmt.2009.029387.

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37

Pagès-Zamora, Alba, and Miguel A. Lagunas. "Fourier models for non-linear signal processing." Signal Processing 76, no. 1 (1999): 1–16. http://dx.doi.org/10.1016/s0165-1684(98)00243-6.

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38

Antoniadis, I., K. Benakli, and A. Laugier. "D-brane models with non-linear supersymmetry." Nuclear Physics B 631, no. 1-2 (2002): 3–42. http://dx.doi.org/10.1016/s0550-3213(02)00181-5.

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39

BRUBAKER, NICHOLAS D., and JOHN A. PELESKO. "Non-linear effects on canonical MEMS models." European Journal of Applied Mathematics 22, no. 5 (2011): 455–70. http://dx.doi.org/10.1017/s0956792511000180.

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In modelling electrostatically actuated micro- and nano-electromechanical systems, researchers have typically relied on a small-aspect ratio to form a leading-order theory. In doing so, small gradient terms are dropped. Although this approximation has been fruitful, its consequences have not been investigated. Here, this approximation is re-examined, and a new theory which includes often neglected small curvature terms is presented. Furthermore, the solution set of the new theory is explored for the unit disk domain and compared to the standard theory. Also, the analytical results are compared

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40

Lindström, Ulf. "Generalized =(2,2) supersymmetric non-linear sigma models." Physics Letters B 587, no. 3-4 (2004): 216–24. http://dx.doi.org/10.1016/j.physletb.2004.03.014.

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41

Vigliotti, Andrea, Vikram S. Deshpande, and Damiano Pasini. "Non linear constitutive models for lattice materials." Journal of the Mechanics and Physics of Solids 64 (March 2014): 44–60. http://dx.doi.org/10.1016/j.jmps.2013.10.015.

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42

van Garderen, Kees Jan, Kevin Lee, and M. Hashem Pesaran. "Cross-sectional aggregation of non-linear models." Journal of Econometrics 95, no. 2 (2000): 285–331. http://dx.doi.org/10.1016/s0304-4076(99)00040-8.

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43

Stewart, C., T. Kyriacou, N. Postans, S. E. Jarvis, and A. P. Roberts. "Creating non-linear models of human gait." Gait & Posture 39 (June 2014): S140—S141. http://dx.doi.org/10.1016/j.gaitpost.2014.04.201.

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44

Minkina, Waldemar. "Non-linear models of temperature sensor dynamics." Sensors and Actuators A: Physical 30, no. 3 (1992): 209–14. http://dx.doi.org/10.1016/0924-4247(92)80122-j.

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45

Fujimoto, Takao. "Non-linear leontief models in abstract spaces." Journal of Mathematical Economics 15, no. 2 (1986): 151–56. http://dx.doi.org/10.1016/0304-4068(86)90006-6.

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46

Blasi, A., та R. Collina. "Stability of non-linear homogeneous σ models". Physics Letters B 200, № 1-2 (1988): 98–102. http://dx.doi.org/10.1016/0370-2693(88)91117-3.

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47

Blair, D. P. "Non-linear superposition models of blast vibration." International Journal of Rock Mechanics and Mining Sciences 45, no. 2 (2008): 235–47. http://dx.doi.org/10.1016/j.ijrmms.2007.05.002.

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48

Cooley, Thomas F., William R. Parke, and Siddhartha Chib. "Predictive efficiency for simple non-linear models." Journal of Econometrics 40, no. 1 (1989): 33–44. http://dx.doi.org/10.1016/0304-4076(89)90028-6.

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49

Pemberton, John. "Forecasting with non-linear time series models." Stochastic Processes and their Applications 26 (1987): 198–99. http://dx.doi.org/10.1016/0304-4149(87)90098-6.

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50

Brøns, M., W. Kliem, and S. Markvorsen. "Non-linear models of a vibrating elasticum." Journal of Sound and Vibration 158, no. 1 (1992): 35–43. http://dx.doi.org/10.1016/0022-460x(92)90662-h.

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