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• W4288730871 abstract "In nonparametric analyses, many authors indicate that the kernel density functions work well when the variable is close to the Gaussian shape. This chapter interest is on the improvement the forecastability of the functional nonparametric time series by using a new approach of the parametric power transformation. The choice of the power parameter in this approach is based on minimizing the mean integrated square error of kernel estimation. Many authors have used this criterion in estimating density under the assumption that the original data follow a known probability distribution. In this chapter, the authors assumed that the original data were of unknown distribution and set the theoretical framework to derive a criterion for estimating the power parameter and proposed an application algorithm in two-time series of temperature monthly averages." @default.
• W4288730871 created "2022-07-30" @default.
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• W4288730871 date "2023-01-18" @default.
• W4288730871 modified "2023-09-25" @default.
• W4288730871 title "A New Approach of Power Transformations in Functional Non-Parametric Temperature Time Series" @default.
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• W4288730871 doi "https://doi.org/10.5772/intechopen.105832" @default.
• W4288730871 hasPublicationYear "2023" @default.
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