Add to ORKGDiscrete-time ARMA processes can be placed in a one-to-one correspondence with a set of continuous-time processes that are bounded in frequency by the Nyquist value of ð radians per sample period. It is well known that, if data are sampled from a continuous process of which the maximum frequency exceeds the Nyquist value, then there will be a problem of aliasing. However, if the sampling is too rapid, then other problems will arise that will cause the ARMA estimates to be severely biased. The paper reveals the nature of these problems and it shows how they may be overcome.Stochastic Differential Equations, Band-Limited Stochastic Processes, Oversampling
Decimating a uniformly sampled signal a factor D involves low-pass anti-alias filtering with normali...
Prediction and filtering of continuous-time stochastic processes often require a solver of a continu...
Abstract. There exists a widespread belief that a discrete series of samples does not contain enough...
Discrete-time ARMA processes can be placed in a one-to-one correspondence with a set of continuous-t...
In the theory of stochastic differential equations, it is commonly assumed that the forcing function...
International audienceWe consider the problem of reconstructing a wide sense stationary band-limited...
The problem of estimating continuous-domain autoregressive moving-average processes from sampled dat...
Aliasing gives a lower bound for the sampling rate in ordinary spectral analysis of a time series. I...
For the representation of a bandlimited signal by its discrete samples for several purposes an estim...
Abstract In this paper, fast algorithms for the extrapolation of band-limited signals are presented ...
The phenomenon of aliasing is important when sam-pling analog signals. In cases where the signal is ...
In this paper, we consider nonlinear stochastic systems and intersect ideas from nonlinear control t...
Initial algorithms for computing the Wigner distribution and other time-frequency representations be...
In power spectral estimation of a continuous band-limited random process, one must usually estimate ...
Non-linear time series, continuous-time ARMA process, threshold model, stochastic differential equat...
Decimating a uniformly sampled signal a factor D involves low-pass anti-alias filtering with normali...
Prediction and filtering of continuous-time stochastic processes often require a solver of a continu...
Abstract. There exists a widespread belief that a discrete series of samples does not contain enough...
Discrete-time ARMA processes can be placed in a one-to-one correspondence with a set of continuous-t...
In the theory of stochastic differential equations, it is commonly assumed that the forcing function...
International audienceWe consider the problem of reconstructing a wide sense stationary band-limited...
The problem of estimating continuous-domain autoregressive moving-average processes from sampled dat...
Aliasing gives a lower bound for the sampling rate in ordinary spectral analysis of a time series. I...
For the representation of a bandlimited signal by its discrete samples for several purposes an estim...
Abstract In this paper, fast algorithms for the extrapolation of band-limited signals are presented ...
The phenomenon of aliasing is important when sam-pling analog signals. In cases where the signal is ...
In this paper, we consider nonlinear stochastic systems and intersect ideas from nonlinear control t...
Initial algorithms for computing the Wigner distribution and other time-frequency representations be...
In power spectral estimation of a continuous band-limited random process, one must usually estimate ...
Non-linear time series, continuous-time ARMA process, threshold model, stochastic differential equat...
Decimating a uniformly sampled signal a factor D involves low-pass anti-alias filtering with normali...
Prediction and filtering of continuous-time stochastic processes often require a solver of a continu...
Abstract. There exists a widespread belief that a discrete series of samples does not contain enough...