In this study, we presented the most commonly employed net photosynthetic light-response curves (PN/I curves) fitted by the Solver function of Microsoft Excel. Excel is attractive not only due to its wide availability as a part of the Microsoft Office suite but also due to the increased level of familiarity of undergraduate students with this tool as opposed to other statistical packages. In this study, we explored the use of Excel as a didactic tool which was built upon a previously published paper presenting an Excel Solver tool for calculation of a net photosynthetic/chloroplastic CO2-response curve. Using the Excel spreadsheets accompanying this paper, researchers and students can quickly and easily choose the best fitted PN/I curve, selecting it by the minimal value of the sum of the squares of the errors. We also criticized the misuse of the asymptotic estimate of the maximum gross photosynthetic rate, the light saturation point estimated at a specific percentile of maximum net photosynthetic rate, and the quantum yield at zero photosynthetic photon flux density and we proposed the replacement of these variables by others more directly linked to plant ecophysiology. and F. de A. Lobo ... [et al.].
A random process (field) with given parametrized mean and covariance function is observed at a finite number of chosen design points . The information about its parameters is measured via the Fisher information matrix (for normally distributed observations) or using information functionals depending on that matrix. Conditions are stated, under which the contribution of one design point to this information is zero. Explicit expressions are obtained for the amount of information coming from a selected subset of a given design. Relations to some algorithms for optimum design of experiments in case of correlated observations are indicated.
The longitudinal regression model Zji=m(θ0,Xi(Tji))+εji, where Zji is the jth measurement of the ith subject at random time Tji, m is the regression function, Xi(Tji) is a predictable covariate process observed at time Tji and εji is a noise, is studied in marked point process framework. In this paper we introduce the assumptions which guarantee the consistency and asymptotic normality of smooth M-estimator of unknown parameter θ0
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