Modeling Bromus diandrus Seedling Emergence Using Nonparametric Estimation
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Hydrothermal time (HTT) is a valuable environmental index to predict weed emergence. In this paper, we focus on the problem of predicting weed emergence given some HTT observations from a distribution point of view. This is an alternative approach to classical parametric regression, often employed in this framework. The cumulative distribution function (cumulative emergence) of the cumulative hydrothermal time (CHTT) is considered for this task. Due to the monitoring process, it is not possible to observe the exact emergence time of every seedling. On the contrary, these emergence times are observed in an aggregated way. To address these facts, a new nonparametric distribution function estimator has been proposed. A bootstrap bandwidth selection method is also presented. Moreover, bootstrap techniques are also used to develop simultaneous confidence intervals for the HTT cumulative distribution function. The proposed methods have been applied to an emergence data set of Bromus diandrus.
Key WordsHydrothermal time Interval-censorship Nonparametric distribution estimation Bromus diandrus
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- Bonferroni, C. E. (1935), “Il Calcolo Delle Assicurazioni Su Gruppi di Teste,” in Studi in Onore del Professore Salvatore Ortu Carboni, Rome, pp. 13–60. Google Scholar
- Davison, A. C., and Hinkley, D. V. (1997), Bootstrap Methods and Their Applications, Cambridge: Cambridge Univ. Press. Google Scholar
- Gonzalez-Andujar, J. L., Fernandez-Quintanilla, C., Bastida, F., Calvo, R., Gonzalez-Diaz, L., Izquierdo, J., Lezaun, J. A., Perea, F., Sanchez Del Arco, M. J., and Urbano, J. (2010), “Field Evaluation of a Decision Support System for Avena sterilis ssp. ludoviciana Control in Winter Wheat,” Weed Research, 50, 83–88. CrossRefGoogle Scholar
- Miller, R. G. (1991), Simultaneous Statistical Inference, New York: Springer. Google Scholar
- Peto, R. (1973), “Experimental Survival Curves for Interval-Censored Data,” Journal of the Royal Statistical Society, Series C, 22, 86–91. Google Scholar
- Seber, G. A. F., and Wild, C. J. (2003), Nonlinear Regression, Hoboken: Wiley-Interscience. Google Scholar