FUW TRENDS IN SCIENCE & TECHNOLOGY JOURNAL
(A Peer Review Journal)
e–ISSN: 2408–5162; p–ISSN: 2048–5170
keywords: Denoising, measurement error, Monte-Carlo, non-linear regression model, production function
The observed data from various fields are frequently characterized by measurement error and this has been a challenging problem to constructing consistent estimators of the parameters in a nonlinear regression model. In the study, simulated data under three (3) sample sizes (i.e. 32, 256 and 1024) were used, applying Epanechnikov kernel, Gaussian kernel, Wavelet and Polynomial Spline on noisy data. The study revealed the performances of denoised nonlinear estimators under different sample sizes and comparison was made using the mean squared error criterion. The result of the studies showed that the denoised nonlinear least squares estimator (DNLS) is the best under each sample size considered.
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