in some statistical process control applications, quality of a process or product can be characterized by a relationship between a response and one or more independent variables, which is typically referred to a profile. in this paper, polynomial profiles are considered to monitor processes in which there is a first order autoregressive relation between the error terms in each profile. a remedi...

We propose a new class of observation driven time series models that we refer to as Generalized Autoregressive Score (GAS) models. The driving mechanism of the GAS model is the scaled likelihood score. This provides a unified and consistent framework for introducing time-varying parameters in a wide class of non-linear models. The GAS model encompasses other well-known models such as the genera...

High-resolution spectrum estimation techniques have been extensively studied in recent publications. Knowledge of the noise variance is vital for spectrum estimation from noise-corrupted observations. This paper presents the use of noise compensation and data extrapolation for spectrum estimation. We assume that the observed data sequence can be represented by a set of autoregressive parameters...

In this paper we consider nonhomogeneous autoregressive processes which are special cases of the vector-valued autoregressive processes considered by Anderson (1978) for the analysis of panel survey data. We point out that, for a nonhomogeneous autoregressive process of order higher than one, the least-squares estimates cannot be obtained unless repeated measurements are made on the time series...

in this article, the discrete time state space model with first-order autoregressive dependent process noise is considered and the recursive method for filtering, prediction and smoothing of the hidden state from the noisy observation is designed. the explicit solution is obtained for the hidden state estimation problem. finally, in a simulation study, the performance of the designed method ...

This paper proposes a simple approach to analyzing profit dynamics which allows for time-varying persistence of profits. The time series model is a simple autoregressive process where the dynamics of the persistence parameter follow an autoregressive or random walk process. Using the longest time series available on profits for six US firms (Archer-Daniels-Midland , Avon, Coca Cola, Johnson & J...

We consider a heteroscedastic nonparametric regression model with an autoregressive error process of finite known order p. The heteroscedasticity is incorporated using a scaling function defined at uniformly spaced design points on an interval [0,1]. We provide an innovative nonparametric estimator of the variance function and establish its consistency and asymptotic normality. We also propose ...

This paper is concerned with the construction of bootstrap prediction intervals for autoregressive fractionally integrated movingaverage processes which is a special class of long memory time series. For linear short-range dependent time series, the bootstrap based prediction interval is a good nonparametric alternative to those constructed under parameter assumptions. In the long memory case, ...