120 resultados para Cryptography Statistical methods


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Pós-graduação em Agronomia (Energia na Agricultura) - FCA

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Pós-graduação em Agronomia (Energia na Agricultura) - FCA

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Pós-graduação em Agronomia (Genética e Melhoramento de Plantas) - FCAV

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Pós-graduação em Agronomia (Genética e Melhoramento de Plantas) - FCAV

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Pós-graduação em Ciências Biológicas (Zoologia) - IBRC

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The objective of this study was to identify clones of sugarcane with good stability and adaptability in the State of São Paulo, Brazil, and also identifying locations most representative for selection and experimentation. Ten clones and two commercial checks of medium-late maturation were evaluated in the first-ratoon of experiments harvested in August 2009, using bissegmented regression and AMMI (Additive Main Effects and Multiplicative Interaction Analysis) methods. The results of two methods were compared and they evidenced that the clones RB975201, RB975157, RB975932, RB975242 and RB975162 are the most promising, showing higher production when compared to the checks, higher stability observed in one or both statistical methods and broader or more specific adaptability. The environment Tarumã presented higher stability and capacity to discriminate genotypes, allowing an ordering more reliable as compared to the overall mean of the environments tested.

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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The Box-Cox transformation is a technique mostly utilized to turn the probabilistic distribution of a time series data into approximately normal. And this helps statistical and neural models to perform more accurate forecastings. However, it introduces a bias when the reversion of the transformation is conducted with the predicted data. The statistical methods to perform a bias-free reversion require, necessarily, the assumption of Gaussianity of the transformed data distribution, which is a rare event in real-world time series. So, the aim of this study was to provide an effective method of removing the bias when the reversion of the Box-Cox transformation is executed. Thus, the developed method is based on a focused time lagged feedforward neural network, which does not require any assumption about the transformed data distribution. Therefore, to evaluate the performance of the proposed method, numerical simulations were conducted and the Mean Absolute Percentage Error, the Theil Inequality Index and the Signal-to-Noise ratio of 20-step-ahead forecasts of 40 time series were compared, and the results obtained indicate that the proposed reversion method is valid and justifies new studies. (C) 2014 Elsevier B.V. All rights reserved.

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Pós-graduação em Agronomia (Produção Vegetal) - FCAV

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Pós-graduação em Engenharia de Produção - FEB

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Pós-graduação em Genética e Melhoramento Animal - FCAV