Optimal filtering for bilinear system states and its application to terpolymerization process identification.
Optimal linear filtering of general multidimensional Gaussian processes and its application to Laplace transforms of quadratic functionals.
Optimising prediction error among completely monotone covariance sequences.
Original title unknown
Prédiction non paramétrique : étude de l'erreur quadratique du prédictogramme
Prediction of time series by statistical learning: general losses and fast rates
We establish rates of convergences in statistical learning for time series forecasting. Using the PAC-Bayesian approach, slow rates of convergence √ d/n for the Gibbs estimator under the absolute loss were given in a previous work [7], where n is the sample size and d the dimension of the set of predictors. Under the same weak dependence conditions, we extend this result to any convex Lipschitz loss function. We also identify a condition on the parameter space that ensures similar rates for the...
Prediction problems related to a first-order autoregressive process in the presence of outliers
Outliers in a time series often cause problems in fitting a suitable model to the data. Hence predictions based on such models are liable to be erroneous. In this paper we consider a stable first-order autoregressive process and suggest two methods of substituting an outlier by imputed values and then predicting on the basis of it. The asymptotic properties of both the process parameter estimators and the predictors are also studied.
Prédiction scalaire ou prédiction vectorielle pour des processus faiblement stationnaires
Predictions in nonlinear regression models.
Probabilistic finite state automata and time series analysis.
Quadratic estimation from non-independent uncertain observations with coloured noise.
Recursive least-squares quadratic filtering and fixed-point smoothing algorithms for signal estimation from uncertain observations are derived when the uncertainty is modeled by not necessarily independent variables and the observations contain white plus coloured noise. The proposed estimators do not require the knowledge of the state-space of the model generating the signal, but only the moments, up to the fourth one, of the processes involved, along with the probability that the signal exists...
Quelques problèmes linéaires de statistique concernant un processus stochastique de moyenne inconnue
Real-time parameter estimation and output prediction for ARMA-type system models
Reducing false alarms of intensive care online-monitoring systems: an evaluation of two signal extraction algorithms.
Reflection coefficients vs partial autocorrelations.
Régression trigonométrique et plans d'échantillonnage pour expériences simulées
Robust Kalman filter and its application in time series analysis
Robust time series analysis: a survey
Robustness of the best linear unbiased estimator and predictor in linear regression models
If is shown that in linear regression models we do not make a great mistake if we substitute some sufficiently precise approximations for the unknown covariance matrix and covariance vector in the expressions for computation of the best linear unbiased estimator and predictor.
Seasonal time series with missing observations
Popular exponential smoothing methods dealt originally only with equally spaced observations. When time series contains gaps, smoothing constants have to be adjusted. Cipra et al., following Wright’s approach of irregularly spaced observations, have suggested ad hoc modification of smoothing constants for the Holt-Winters smoothing method. In this article the fact that the underlying model of the Holt-Winters method is a certain seasonal ARIMA is used. Minimum mean square error smoothing constants...