A simple procedure is proposed for estimating the coefficients {[psi]} from observations of the linear process X1=[summation operator]xJ=0[psi]JZ1-j, 1=1,2... The method is based on the representation of X1 in terms of the innovations, Xn-Xn, N=1,..., 1, where Xn is the best mean square...

A system is subject to random failure with failure rate k(Xt) dependent upon the level Xt of accumulated damage at time t. Given the replacement cost C and the additional cost K for replacement after failure, an optimum level of damage at which replacement should be made is investigated when the...

We provide a direct proof for consistency and asymptotic normality of Gaussian maximum likelihood estimators for causal and invertible autoregressive moving-average (ARMA) time series models, which were initially established by Hannan [Journal of Applied Probability (1973) vol. 10, pp....

Continuous-time autoregressive moving average (CARMA) processes with a nonnegative kernel and driven by a nondecreasing Lévy process constitute a useful and very general class of stationary, nonnegative continuous-time processes that have been used, in particular, for the modeling of stochastic...

We consider the parametric estimation of the driving Lévy process of a multivariate continuous-time autoregressive moving average (MCARMA) process, which is observed on the discrete time grid (0,h,2h,…). Beginning with a new state space representation, we develop a method to recover the...

In order to predict unobserved values of a linear process with infinite variance, we introduce a linear predictor which minimizes the dispersion (suitably defined) of the error distribution. When the linear process is driven by symmetric stable white noise this predictor minimizes the scale...