Discrete Periodic Sampling with Jitter and Almost Periodically Correlated Processes

The zero-mean process is said to be almost periodically correlated whenever its shifted covariance kernel is almost periodic in t uniformly with respect to . Then it admits a Fourier–Bohr decomposition: . This paper deals with the estimation of the spectral covariance a(λ,τ) from a discrete time observation of the process , when jitter and delay phenomena are present in conjunction with periodic sampling. Under mixing conditions, we establish the consistency and the asymptotic normality of empirical estimators as the sampling time step tends to 0 and the sampling period tends to infinity.      

Spectral analysis of the covariance of the almost periodically correlated processes

This paper deals with the spectrum of the almost periodically correlated (APC) processes defined on . It is established that the covariance kernel of such a process admits a Fourier series decomposition, K(s+t, s) , whose coefficient functions b are the Fourier transforms of complex measures m, , which are absolutely continuous with respect to the measure m_o. Considering the APC strongly harmonizable processes, the spectral covariance of the process can be expressed in terms of these complex measures m.

The usual estimators for the second order situation can be modified to provide consistent estimators of the coefficient functions b from a sample of the process. Whenever the measures m are absolutely continuous with respect to the Lebesgue measure, so m(dλ)=f(λ) dλ, the estimation of the corresponding density functions f is considered. Under hypotheses on the covariance kernel K and on the coefficient functions b, we establish rates of convergence in quadratic mean and almost everywhere of these estimators.     

On likelihood estimation for a discretely observed jump process

We consider the parameter estimation problem for a Markov jump process sampled at periodic epochs with a constant step. Unlike the diffusion case where a closed form of the likelihood function is usually unavailable, we provide here an explicit expression of the likelihood function of the sampled chain. Moreover under suitable ergodicity condition on the jump process, we establish the consistency and the asymptotic normality of the likelihood estimator as the observation period tends to infinity. To cite this article: D. Dehay, J.-f. Yao, C. R. Acad. Sci. Paris, Ser. I 342 (2006).     

Efficient parametric estimation for a signal-plus-noise Gaussian model from discrete time observations

Statistical Inference for Stochastic Processes - This paper deals with the parametric inference for integrated continuous time signals embedded in an additive Gaussian noise and observed at...     

On a class of asymptotically stationary harmonizable processes

We prove that every harmonizable process with σ-finite bimeasure is asymptotically stationary and we give its associated spectral measure.