2005
DOI: 10.2172/877714
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Confidence region estimation techniques for nonlinear regression :three case studies.

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Cited by 5 publications
(3 citation statements)
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“…In this case, the optimization goal was to minimize the mean square difference between the curve and the original points. Parameters such as amplitude and phase shift for the best-fitting sine wave were obtained using optimization techniques available in the scikit-learn library [26]. The sine wave was then used to map new observations to the original data points' space, where the period from 1 January to 31 December each year corresponded to the interval [0, 2𝜋).…”
Section: Rnn Modelmentioning
confidence: 99%
“…In this case, the optimization goal was to minimize the mean square difference between the curve and the original points. Parameters such as amplitude and phase shift for the best-fitting sine wave were obtained using optimization techniques available in the scikit-learn library [26]. The sine wave was then used to map new observations to the original data points' space, where the period from 1 January to 31 December each year corresponded to the interval [0, 2𝜋).…”
Section: Rnn Modelmentioning
confidence: 99%
“…The analysis of the effect of a change in tractor mass distribution was then conducted by comparing the overlap in the prediction bound areas. Indeed, a significant (or full) overlap in the prediction bound area of one of the regression curves with that of another regression curve indicates that the two regression curves are not significantly different [34].…”
Section: Interpolation Of Data Obtained From Experimentsmentioning
confidence: 99%
“…To this end, a reference field operation was simulated through a procedure that involved the estimation of the net traction ratio exerted by the implement and the determination of the tractor The analysis of the effect of a change in tractor mass distribution was then conducted by comparing the overlap in the prediction bound areas. Indeed, a significant (or full) overlap in the prediction bound area of one of the regression curves with that of another regression curve indicates that the two regression curves are not significantly different [34].…”
Section: Field Productivity and Fuel Consumption Predictionmentioning
confidence: 99%