Abstract
Estimation of unknown parameters in exponential models by linear and nonlinear fitting methods is discussed. Based on the extreme value theorem and Taylor series expansion, it is proved theoretically that the parameters estimated by the linear fitting method alone cannot minimize the sum of the squared residual errors in the measurement data when measurement noise is involved in the data. Numerical simulation is performed to compare the performance of the linear and nonlinear fitting methods. Simulation results show that the linear method can obtain only a suboptimal estimate of the unknown parameters and that the nonlinear method gives more accurate results. Application of the fitting methods is demonstrated where the water spectral attenuation coefficient is estimated from underwater images and imaging distances, which supports the improvement in the accuracy of parameter estimation by the nonlinear fitting method.
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Adi, N.S., 2015. Characterisation of the light environment and biophysical parameters of seagrass using remote sensing. PhD Thesis, The University of Queensland, Brisbane, Australia, p.23–38. http://dx.doi.org/10.14264/uql.2015.998
Åhlén, J., Bengtsson, E., Sundgren, D., 2006. Evaluation of underwater spectral data for color correction applications. Proc. 5th WSEAS Int. Conf. on Circuits, Systems, Electronics, Control & Signal Processing, p.321–326.
Bardaji, R., Sánchez, A.M., Simon, C., et al., 2016. Estimating the underwater diffuse attenuation coefficient with a low-cost instrument: the KdUINO DIY buoy. Sensors, 16(3): 373–387. http://dx.doi.org/10.3390/s16030373
Floudas, C.A., Pardalos, P.M., 2001. Encyclopedia of Optimization. Kluwer Academic Publishers, Norwell, p.1129–1134. http://dx.doi.org/10.1007/0-306-48332-7
Guo, Y.L., Song, H., Liu, H.B., et al., 2016. Model-based restoration of underwater spectral images captured with narrowband filters. Opt. Expr., 24(12): 13101–13120. http://dx.doi.org/10.1364/OE.24.013101
Hartley, H.O., 1961. The modified Gauss-Newton method for the fitting of nonlinear regression functions by least squares. Technometrics, 3(2): 269–280. http://dx.doi.org/10.2307/1266117
He, Q.R., Li, L.P., 2015. Algorithm study on reducing frequency measurement variance of acousto-optic spectrum analyzer. Infrar. Laser Eng., 44(5): 1564–1568(in Chinese).
Kaeli, J.W., Singh, H., Murphy, C., et al., 2011. Improving color correction for underwater image surveys. Proc. IEEE/MTS Oceans, p.805–810.
Lai, J.S., Yang, B., Lin, D.M., et al., 2013. The allometry of coarse root biomass: log-transformed linear regression or nonlinear regression. PLoS One, 8(10): e77007. http://dx.doi.org/10.1371/journal.pone.0077007
Levenberg, K., 1944. A method for the solution of certain non-linear problems in least squares. Q. Appl. Math., 2: 164–168. http://dx.doi.org/10.1090/qam/10666
Lieb, S.G., 1997. Simplex method of nonlinear least-squares: a logical complementary method to linear least-square analysis of data. J. Chem. Educat., 74(8): 1008–1011. http://dx.doi.org/10.1021/ed074p1008
Liu, C.Q., Ma, J.S., Shao, X.L., et al., 2013. Detection of Gaussian beam distribution of light intensity. Opt. Instrum., 35(6): 69–73(in Chinese). http://dx.doi.org/10.3969/j.issn.1005-5630.2013.06.014
Marquardt, D.W., 1963. An algorithm for least-squares estimation of nonlinear parameters. J. Soc. Ind. Appl. Math., 11(2): 431–441. http://dx.doi.org/10.1137/0111030
Mascaro, J., Litton, C.M., Hughes, R.F., et al., 2011. Minimizing bias in biomass allometry: model selection and log-transformation of data. Biotropica, 43(6): 649–653. http://dx.doi.org/10.1111/j.1744-7429.2011.00798.x
Novković, D., Nadderd, L., Kandić, A., et al., 2006. Testing the exponential decay law of gold 198Au. Nucl. Instrum. Methods Phys. Res. Sect. A, 566(2): 477–480. http://dx.doi.org/10.1016/j.nima.2006.07.044
Peterson, P., Baker, E., McGaw, B., 2010. International Encyclopedia of Education. Elsevier Science, Oxford, p.339–346.
Schechner, Y.Y., Karpel, N., 2004. Clear underwater vision. Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, p.536–543. http://dx.doi.org/10.1109/CVPR.2004.1315078
Semkow, T.M., 2007. Exponential decay law and nuclear statistics. In: Semkow, T.M., Pommé, S., Jerome, S., et al. (Eds.), Applied Modeling and Computations in Nuclear Science. ACS, Washington DC. http://dx.doi.org/10.1021/bk-2007-0945.ch004
Simon, A., Shanmugam, P., 2016. Estimation of the spectral diffuse attenuation coefficient of downwelling irradiance in inland and coastal waters from hyperspectral remote sensing data: validation with experimental data. Int. J. Appl. Earth Observ. Geoinform., 49: 117–125. http://dx.doi.org/10.1016/j.jag.2016.02.003
Swinehart, D.F., 1962. The Beer-Lambert law. J. Chem. Educat., 39(7): 333–335. http://dx.doi.org/10.1021/ed039p333
Vickery, P.J., 2005. Simple empirical models for estimating the increase in the central pressure of tropical cyclones after landfall along the coastline of the United States. J. Appl. Meteorol., 44: 1807–1826. http://dx.doi.org/10.1175/JAM2310.1
Wei, J.W., Lee, Z.P., 2013. Model of the attenuation coefficient of daily photosynthetically available radiation in the upper ocean. Methods Oceanogr., 8: 56–74. http://dx.doi.org/10.1016/j.mio.2013.12.001
Xiao, X., White, E.P., Hooten, M.B., et al., 2011. On the use of log-transformation vs. nonlinear regression for analyzing biological power laws. Ecology, 92(10): 1887–1894. http://dx.doi.org/10.1890/11-0538.1
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Project supported by the National Natural Science Foundation of China (Nos. 61605038 and 11304278), the National High-Tech R&D Program (863) of China (No. 2014AA093400), and the Open Fund of State Key Laboratory of Satellite Ocean Environment Dynamics (No. SOED1606)
ORCID: Hui HUANG, http://orcid.org/0000-0003-1204-3684
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Yang, P., Wu, Cp., Guo, Yl. et al. Parameter estimation in exponential models by linear and nonlinear fitting methods. J. Zhejiang Univ. - Sci. C 18, 434–444 (2017). https://doi.org/10.1631/FITEE.1601683
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DOI: https://doi.org/10.1631/FITEE.1601683