Comparison between Lift Transform and Adaptive Lift in Estimating the Nonparametric Regression Function with An Application
DOI:
https://doi.org/10.31272/jae.i143.1203Keywords:
Discrete Wavelet Transformation, non-parametric regression, Lifting Transformation, Adaptive lifting TransformatioAbstract
In this research, the lift transformations are studied, and the possibility of employing the most efficient ones in processing and analysing the signal is demonstrated to improve it by removing noise from it and then estimating the nonparametric regression function. We will present some transformation methods and the mechanism of their application to get rid of the noise in the signal since both lift transformation methods were taken. (Lifting Transformation) and Adaptive Lifting Transformation were compared with each other using AMSE and with different test functions, and the best was chosen. It turned out that the Adaptive Lifting Transformation method was the best, followed by the Lifting Transformation method (LIFTINGW), which has different functions. Testing and sample sizes, as the mentioned methods, were applied to accurate data, represented by the financial liquidity ratio as an explanatory variable and the interest rates on short-term loans as a dependent variable for the period from (2013 to 2022), as the mentioned methods proved their efficiency in interpreting the influence relationship of the mentioned variables
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[2] AbduAlkareem Mahdi, M. S., & Hamza, S. K. (2022). Using the wavelet analysis to estimate the nonparametric regression model in the presence of associated errors. International journal of nonlinear analysis and applications, 13(1), 1855-1862.
[3] Alsberg, B. K., Woodward, A. M., & Kell, D. B. (1997). An introduction to wavelet transforms for chemometricians: A time-frequency approach. Chemometrics and intelligent laboratory systems, 37(2), 215-239. DOI: https://doi.org/10.1016/S0169-7439(97)00029-4
[4] Claypoole, R. L., Baraniuk, R. G., & Nowak, R. D. (1998, May). Adaptive wavelet transforms via lifting. In Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP'98 (Cat. No. 98CH36181) (Vol. 3, pp. 1513-1516). IEEE. DOI: https://doi.org/10.1109/ICASSP.1998.681737
[5] David, Adeyanju Olanrewaju, Samuel, Olabode Oluwayinka," Liquidity Management and Commercial Banks’ Profitability in Nigeria ", Research Journal of Finance and Accounting , Vol 2, No 7/8, 2012, Redeemer’s University.
[6] Hamza, S. K., & Ali, S. (2022). Estimation of nonparametric regression function using shrinkage wavelet and different mother functions. Periodicals of Engineering and Natural Sciences, 10(6), 96-103. DOI: https://doi.org/10.21533/pen.v10.i6.912
[7] Knight, M. I., Nason, G. P., & Nunes, M. A. (2017). A wavelet lifting approach to long-memory estimation. Statistics and Computing, 27(6), 1453-1471. DOI: https://doi.org/10.1007/s11222-016-9698-2
[8] Knight, M., & Nunes, M. (2008). An adaptive lifting algorithm and applications. In Proceedings of the 56th Session of the ISI (pp. 166-173). Instituto Nacional de Estatıstica.
[9] Nunes, M. A., Knight, M. I., & Nason, G. P. (2006). Adaptive lifting for nonparametric regression. Statistics and Computing, 16, 143-159. DOI: https://doi.org/10.1007/s11222-006-6560-y
[10] Sweldens, W. (1995, September). Lifting scheme: a new philosophy in biorthogonal wavelet constructions. In Wavelet applications in signal and image processing III (Vol. 2569, pp. 68-79). SPIE. DOI: https://doi.org/10.1117/12.217619
[11] Uytterhoeven, G., Roose, D., & Bultheel, A. (1999). Integer wavelet transforms using the lifting scheme. In 3rd World Multiconference on Circuits, Systems, Communications and Computers, Date: 1999/07/04-1999/07/08, Location: Athens, Greece (pp. 198-200). IEEE/IMACS/OTE.
[12] Wu, Y., Wang, G., & Nie, N. (2001, March). Adaptive lifting scheme of wavelet transforms for image compression. In Wavelet Applications VIII (Vol. 4391, pp. 154-160). SPIE. DOI: https://doi.org/10.1117/12.421192
[13] Yang, Z., Cai, L., Gao, L., & Wang, H. (2012). Adaptive redundant lifting wavelet transform based on fitting for fault feature extraction of roller bearings. Sensors, 12(4), 4381-4398. DOI: https://doi.org/10.3390/s120404381
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