… of stationary processes with missing observations. We present a class of estimators based
on the sample covariances, and propose an asymptotically optimal estimator in this class. The …

… handle the estimation of 0 when some of the X(n) are not observed. (… -parametric estimates
of the autocovariances and spectrum of X(n). We propose to use them in parameter estimation…

… -parametric regression estimator supported by the software of the book. Note that the …
estimator is robust to possible missing observations. Then we apply the spectral density estimator …

… is (almost) periodic, parameter estimation algorithms are developed based on second- and
higher order cumulants of the resulting cyclostationary observations, which may be corrupted …

… of estimation methods for system output signal with missing … parameter estimation problem
under missing observations … on the assumption that the observed system input/output data are …

… Therefore, we suggest estimating the parameter by pairs of … is missing randomly rather
than monotonously. Therefore, to surpass this challenge, we propose estimating the parameters …

… to estimate the parameters of missing data systems, and establish convergence properties
for the parameter and missing output estimation in … in the case of missing observations. IEEE …

… for the missing observations and we briefly discuss ways to obtain ML estimates. Our general
… and present some results on the effect on parameter estimates in large samples of using …

… of estimating the parameters of a multivariate data set which contains blocks of missing
observations. … estimation problem into smaller estimation problems by factoring the likelihood of …

… The complexity of the parameter estimation problem considered in this article arises due to
unknown nonlinearities, and presence of unmeasured latent variables. If the latent variables …