Are volatility indices in international stock markets forward looking?
Arithmetizing continuous distributions: numerical aspects.
ARMA models with nonstationary white noise
Artificial neural networks in time series forecasting: a comparative analysis
Artificial neural networks (ANN) have received a great deal of attention in many fields of engineering and science. Inspired by the study of brain architecture, ANN represent a class of non-linear models capable of learning from data. ANN have been applied in many areas where statistical methods are traditionally employed. They have been used in pattern recognition, classification, prediction and process control. The purpose of this paper is to discuss ANN and compare them to non-linear time series...
Aspects of analysis of multivariate failure time data.
Multivariate failure time data arise in various forms including recurrent event data when individuals are followed to observe the sequence of occurrences of a certain type of event; correlated failure time when an individual is followed for the occurrence of two or more types of events for which the individual is simultaneously at risk, or when distinct individuals have depending event times; or more complicated multistate processes where individuals may move among a number of discrete states over...
Aspects of control for the normal Markov processes.
Assessing influence in survival data with a cured fraction and covariates.
Diagnostic methods have been an important tool in regression analysis to detect anomalies, such as departures from error assumptions and the presence of outliers and influential observations with the fitted models. Assuming censored data, we considered a classical analysis and Bayesian analysis assuming no informative priors for the parameters of the model with a cure fraction. A Bayesian approach was considered by using Markov Chain Monte Carlo Methods with Metropolis-Hasting algorithms steps to...
Association entre variables qualitatives ordinales «nettes» ou «floues»
Asymmetric recursive methods for time series
The problem of asymmetry appears in various aspects of time series modelling. Typical examples are asymmetric time series, asymmetric error distributions and asymmetric loss functions in estimating and predicting. The paper deals with asymmetric modifications of some recursive time series methods including Kalman filtering, exponential smoothing and recursive treatment of Box-Jenkins models.
Asymmetric semilinear copulas
We complement the recently introduced classes of lower and upper semilinear copulas by two new classes, called vertical and horizontal semilinear copulas, and characterize the corresponding class of diagonals. The new copulas are in essence asymmetric, with maximum asymmetry given by . The only symmetric members turn out to be also lower and upper semilinear copulas, namely convex sums of and .
Asymptotic analysis for bifurcating autoregressive processes via a martingale approach.
Asymptotic analysis of minimum volume confidence regions for location-scale families
An asymptotic analysis, when the sample size n tends to infinity, of the optimal confidence region established in Czarnowska and Nagaev (2001) is considered. As a result, two confidence regions, both close to the optimal one when n is sufficiently large, are suggested with a mild assumption on the distribution of a location-scale family.
Asymptotic approximations to the Bayes posterior risk.
Asymptotic behavior of the empirical process for gaussian data presenting seasonal long-memory
We study the asymptotic behavior of the empirical process when the underlying data are gaussian and exhibit seasonal long-memory. We prove that the limiting process can be quite different from the limit obtained in the case of regular long-memory. However, in both cases, the limiting process is degenerated. We apply our results to von–Mises functionals and -Statistics.
Asymptotic behavior of the Empirical Process for Gaussian data presenting seasonal long-memory
We study the asymptotic behavior of the empirical process when the underlying data are Gaussian and exhibit seasonal long-memory. We prove that the limiting process can be quite different from the limit obtained in the case of regular long-memory. However, in both cases, the limiting process is degenerated. We apply our results to von–Mises functionals and U-Statistics.
Asymptotic behavior of the likelihood function of covariance matrices of spatial Gaussian processes.
Asymptotic behaviour of a BIPF algorithm with an improper target
The BIPF algorithm is a Markovian algorithm with the purpose of simulating certain probability distributions supported by contingency tables belonging to hierarchical log-linear models. The updating steps of the algorithm depend only on the required expected marginal tables over the maximal terms of the hierarchical model. Usually these tables are marginals of a positive joint table, in which case it is well known that the algorithm is a blocking Gibbs Sampler. But the algorithm makes sense even...
Asymptotic behaviour of an estimator based on Rao's divergence
Asymptotic behaviour of empirical multiinformation
Asymptotic behaviour of the likelihood ratio test under presence of deviations of the model