Recursive linearly constrained minimum variance estimator in linear models with non-stationary constraints

The principle of the approach is based on a new theorem that made it possible to compute recursively the set of all state vectors that are consistent with some bounded errors and

First we apply our graphical method to the case of data set with one single outlier, either in the explanatory variable or/and in the response variable.. Second we apply the

It is worth noting that the continuous variation of V / z is monitored by the estimation algorithm during level changes, and that the parameters accuracy can also be

Keywords : Functional data, recursive kernel estimators, regression function, quadratic error, almost sure convergence, asymptotic normality.. MSC : 62G05,

Then, the ability of our recursive averaged estimator to deal with large samples of very high-dimensional data is illustrated on the robust estimation of television audience

In this article, we show that for nonlinear mixed effects models with Gaussian random effects, Expectation Maximization (EM) like algorithms for the computation of the

This note shows that the asymptotic variance of Chen’s [Econometrica, 70, 4 (2002), 1683–1697] two-step estimator of the link function in a linear transformation model depends on

In order to solve this kind of problem, different approach can be found, such as model inversion (Sil- verman, 1969), Unknown Input Observers (Darouach et al., 1994) and Filters