Asymptotic study of the density associated to a filter of infinite order. (Étude asymptotique de la densité associée à un filtre d'ordre infini.)
Asymptotically optimal filtering in linear systems with fractional Brownian noises.
In this paper, the filtering problem is revisited in the basic Gaussian homogeneous linear system driven by fractional Brownian motions. We exhibit a simple approximate filter which is asymptotically optimal in the sense that, when the observation time tends to infinity, the variance of the corresponding filtering error converges to the same limit as for the exact optimal filter.
Banach Algebra Methods in Prediction Theory.
Bayesian and Frequentist Two-Sample Predictions of the Inverse Weibull Model Based on Generalized Order Statistics
2000 Mathematics Subject Classification: 62E16,62F15, 62H12, 62M20.This paper is concerned with the problem of deriving Bayesian prediction bounds for the future observations (two-sample prediction) from the inverse Weibull distribution based on generalized order statistics (GOS). Study the two side interval Bayesian prediction, point prediction under symmetric and asymmetric loss functions and the maximum likelihood (ML) prediction using "plug-in" procedure for future observations from the inverse...
Combining forecasts using the least trimmed squares
Employing recently derived asymptotic representation of the least trimmed squares estimator, the combinations of the forecasts with constraints are studied. Under assumption of unbiasedness of individual forecasts it is shown that the combination without intercept and with constraint imposed on the estimate of regression coefficients that they sum to one, is better than others. A numerical example is included to support theoretical conclusions.
Comparison of efficient seasonal indexes.
Computational intensive methods for prediction and imputation in time series analysis
One of the main goals in times series analysis is to forecast future values. Many forecasting methods have been developed and the most successful are based on the concept of exponential smoothing, based on the principle of obtaining forecasts as weighted combinations of past observations. Classical procedures to obtain forecast intervals assume a known distribution for the error process, what is not true in many situations. A bootstrap methodology can be used to compute distribution free forecast...
Conditions for the Optimality of Exponential Smoothing Forecast Procedures.
Construction automatique et continue de prévisions
Data imputation in switchback designs using a mixed model with correlated errors.
Development of the kriging method with application
This paper describes a modification of the kriging method for working with the square root transformation of a spatial random process. We have developed this method for the situation where the spatial process observed is not supposed to be stationary but the assumption is that its square root is a second order stationary spatial random process. Consequently this method is developed for estimating the integral of the process observed and finally some application of the method is given to data from...
Discrete time nonlinear filters with informative observations are stable.
Discrimination between nonstationary and nearly nonstationary processes, and its effect on forecasting
Discrimination between nonstationary and nearly nonstationary processes, and its effect on forecasting
Dynamic credibility with outliers and missing observations
In actuarial practice the credibility models must face the problem of outliers and missing observations. If using the -estimation principle from robust statistics in combination with Kalman filtering one obtains the solution of this problem that is acceptable in the numerical framework of the practical actuarial credibility. The credibility models are classified as static and dynamic in this paper and the shrinkage is used for the final ratemaking.
Efficacités comparées de certaines méthodes de prédiction pour un ARMA perturbé
Efficiency of some algorithms for prediction in finite stationary time series
Important characteristics of any algorithm are its complexity and speed in real calculations. From this point of view, we analyze some algorithms for prediction in finite stationary time series. First, we review results developed by P. Bondon [1] and then, we derive the complexities of Levinson and a new algorithm. It is shown that the time needed for real calculations of predictions is proportional to the theoretical complexity of the algorithm. Some practical recommendations for the selection...
EKF-based dual synchronization of chaotic colpitts circuit and Chua’s circuit
In this paper, dual synchronization of a hybrid system containing a chaotic Colpitts circuit and a Chua’s circuit, connected by an additive white Gaussian noise (AWGN) channel, is studied via numeric simulations. The extended Kalman filter (EKF) is employed as the response system to achieve the dual synchronization. Two methods are proposed and investigated. The first method treats the combination of a Colpitts circuit and a Chua’s circuit as a higher- dimensional system, while the second method...
Estimació del pol i de la variància del soroll d'un model AR (1) mitjançant filtratge no lineal.
La estimación de los parámetros asociados a un proceso ARMA puede plantearse como un problema de filtrado no lineal. Para determinar un estimador recursivo de estos parámetros se define un vector de estado ampliado que incluye las variables de estado y los parámetros a estimar. Con un enfoque bayesiano se determina la distribución a posteriori del vector de estado ampliado. La síntesis del filtro no lineal permite: i) estimar los parámetros y determinar su precisión para un tamaño de muestra dado,...
Estimating Parameters of a Multiplicative Seasonal ARIMA Model Using Prediction Error Method Algorithm