By Engler D. A.
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Additional info for A pseudolikelihood approach for simultaneous analysis of array comparative genomic hybridizations (2
One-day ahead forecasts for the hourly wind energy production and the hourly demand or price are also avaliable. Our aim is to predict the 24 hourly residual demand curves for all the days in eight different weeks along 2009. The learning sample considered for the whole forecasting process consists of 58 days (not necessarily consecutive). The whole sample is used to select the smoothing parameter and the semi-norm, while only the last 34 observations are used to build the predictor itself. The semi-norm used is the L2 norm of the q-th derivative (q = 0, 1, 2) and q has been selected by minimizing some cross-validation criterion.
Optimal Offering Strategies for Generation Companies Operating in Electricity Spot Markets. IEEE Transactions on Power Systems 19, 745–753 (2004) 8. : Linear Processes in Function Spaces: Theory and Applications. Lecture Notes in Statistics, 149, Springer (2000) 9. : Nonparametric vs parametric forecasting in time series: a computational point of view. Applied Stochastic Models and Data Analysis 9, 215–229 (1993) 10. : Semiparametric models with functional responses in a model assisted survey sampling setting.
5) 2 Functional Prediction for the Residual Demand in Electricity Spot Markets 13 It should be noted that βh is the ordinary least squares estimator obtained when one linearly links the vector of response variables χh with the matrix of covariates Xh . It is worth mentioning that kernel estimation is used to obtain both χh and Xh . Actually, both terms are computed as some nonparametric residuals. 4) mSFPL (χ ) = h n−1 ∑ wh (χ , χi ) χi+1 (•) − xTi+1 βh (•) . 4)) could be obtained by means of wavelet-kernel approaches (see Antoniadis et al 2006) or local linear functional procedures (see Aneiros-P´erez, Cao and Vilar-Fern´andez 2010), among others.
A pseudolikelihood approach for simultaneous analysis of array comparative genomic hybridizations (2 by Engler D. A.