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Poisson sampling for spectral estimation in periodically correlated processes

Vincent Monsan — 1994

Applicationes Mathematicae

We study estimation problems for periodically correlated, non gaussian processes. We estimate the correlation functions and the spectral densities from continuous-time samples. From a random time sample, we construct three types of estimators for the spectral densities and we prove their consistency.

Minimum distance estimator for a hyperbolic stochastic partial differentialequation

Vincent Monsan; Modeste N'zi — 2000

Applicationes Mathematicae

We study a minimum distance estimator in $L_{2}$ -norm for a class ofnonlinear hyperbolic stochastic partial differential equations, driven by atwo-parameter white noise. The consistency and asymptotic normality of thisestimator are established under some regularity conditions on thecoefficients. Our results are applied to the two-parameterOrnstein-Uhlenbeck process.

Spectral density estimation for $p$ -adic stationary processes

Mustapha Rachdi; Vincent Monsan — 1998

Annales mathématiques Blaise Pascal

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Poisson sampling for spectral estimation in periodically correlated processes

Minimum distance estimator for a hyperbolic stochastic partial differentialequation

Spectral density estimation for $p$ -adic stationary processes

Advanced Search

Formula preview

Currently displaying 1 – 3 of 3

Poisson sampling for spectral estimation in periodically correlated processes

Minimum distance estimator for a hyperbolic stochastic partial differentialequation

Spectral density estimation for p -adic stationary processes

Spectral density estimation for $p$ -adic stationary processes