756 research outputs found
A SARIMAX coupled modelling applied to individual load curves intraday forecasting
A dynamic coupled modelling is investigated to take temperature into account
in the individual energy consumption forecasting. The objective is both to
avoid the inherent complexity of exhaustive SARIMAX models and to take
advantage of the usual linear relation between energy consumption and
temperature for thermosensitive customers. We first recall some issues related
to individual load curves forecasting. Then, we propose and study the
properties of a dynamic coupled modelling taking temperature into account as an
exogenous contribution and its application to the intraday prediction of energy
consumption. Finally, these theoretical results are illustrated on a real
individual load curve. The authors discuss the relevance of such an approach
and anticipate that it could form a substantial alternative to the commonly
used methods for energy consumption forecasting of individual customers.Comment: 17 pages, 18 figures, 2 table
A sharp analysis on the asymptotic behavior of the Durbin-Watson statistic for the first-order autoregressive process
The purpose of this paper is to provide a sharp analysis on the asymptotic
behavior of the Durbin-Watson statistic. We focus our attention on the
first-order autoregressive process where the driven noise is also given by a
first-order autoregressive process. We establish the almost sure convergence
and the asymptotic normality for both the least squares estimator of the
unknown parameter of the autoregressive process as well as for the serial
correlation estimator associated to the driven noise. In addition, the almost
sure rates of convergence of our estimates are also provided. It allows us to
establish the almost sure convergence and the asymptotic normality for the
Durbin-Watson statistic. Finally, we propose a new bilateral statistical test
for residual autocorrelation
A Robbins-Monro procedure for estimation in semiparametric regression models
This paper is devoted to the parametric estimation of a shift together with
the nonparametric estimation of a regression function in a semiparametric
regression model. We implement a very efficient and easy to handle
Robbins-Monro procedure. On the one hand, we propose a stochastic algorithm
similar to that of Robbins-Monro in order to estimate the shift parameter. A
preliminary evaluation of the regression function is not necessary to estimate
the shift parameter. On the other hand, we make use of a recursive
Nadaraya-Watson estimator for the estimation of the regression function. This
kernel estimator takes into account the previous estimation of the shift
parameter. We establish the almost sure convergence for both Robbins-Monro and
Nadaraya--Watson estimators. The asymptotic normality of our estimates is also
provided. Finally, we illustrate our semiparametric estimation procedure on
simulated and real data.Comment: Published in at http://dx.doi.org/10.1214/12-AOS969 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
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Democracy is explained by giving the power to the government, with the consent of citizens, expressing their will, directly or indirectly through voting. Promoting and respecting individual and collective rights and freedoms is the foundation of democracy. Citizen participation is remarkable on two levels in the European government: one, as a citizen of European Union member state, in which exercises the rights and freedoms, the second as a European citizen (in agreement with the Treaty of Maastricht in 1993) which has claimed rights and freedoms recognized by law. Citizens of EU Member States elect, directly or indirectly, representatives (national) in the Community institutions to represent their interests.democracy, individual liberties, individual rights, European governance, fundamentals rights, European Constitution, European Citizen
Asymptotic results for empirical measures of weighted sums of independent random variables
We prove that if a rectangular matrix with uniformly small entries and
approximately orthogonal rows is applied to the independent standardized random
variables with uniformly bounded third moments, then the empirical CDF of the
resulting partial sums converges to the normal CDF with probability one. This
implies almost sure convergence of empirical periodograms, almost sure
convergence of spectra of circulant and reverse circulant matrices, and almost
sure convergence of the CDF's generated from independent random variables by
independent random orthogonal matrices.
For special trigonometric matrices, the speed of the almost sure convergence
is described by the normal approximation and by the large deviation principle
A Durbin-Watson serial correlation test for ARX processes via excited adaptive tracking
We propose a new statistical test for the residual autocorrelation in ARX
adaptive tracking. The introduction of a persistent excitation in the adaptive
tracking control allows us to build a bilateral statistical test based on the
well-known Durbin-Watson statistic. We establish the almost sure convergence
and the asymptotic normality for the Durbin-Watson statistic leading to a
powerful serial correlation test. Numerical experiments illustrate the good
performances of our statistical test procedure
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