Abstract
Consider the problem of estimating growth curve of a bulb crop over time in an agricultural experiment. Harsh environment in experimental land of farm may result in an incomplete and damaged data set. Under certain mild assumptions we model the original data by nonlinear calibration, estimate the growth curve and derive almost sure confidence band for the curve by adopting a technique of Dasgupta (Growth curve and structural equation modeling, 1st edn. Springer proceedings in mathematics & statistics. Springer, New York, 2015). A yield-environment model is proposed and properties of the maximum likelihood estimator therein are investigated. A technique of computing derivative of response curve by nonparametric regression is shown to be strongly consistent for higher order derivatives. Growth data set on a bulb crop is analysed. First derivative, proliferation rate and second derivative curves of calibrated growth for garlic crop are estimated. Associated errors in estimation of the growth curve, its derivative and proliferation rate from composite function, attributed to individual component functions are studied. To a first degree of approximation, error in estimation is symmetric in individual errors, but asymmetric in individual functions.
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Dasgupta, R. (2015). Growth Curve Reconstruction in Damaged Experiment via Nonlinear Calibration. In: Dasgupta, R. (eds) Growth Curve and Structural Equation Modeling. Springer Proceedings in Mathematics & Statistics, vol 132. Springer, Cham. https://doi.org/10.1007/978-3-319-17329-0_7
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DOI: https://doi.org/10.1007/978-3-319-17329-0_7
Publisher Name: Springer, Cham
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