This paper studies the residual empirical process of longand short-memory time series regression models and establishes its uniform expansion under a general framework. The results are applied to the stochastic regression models and unstable autoregressive models. For the long-memory noise, it is shown that the limit distribution of the Kolmogorov–Smirnov test statistic studied in Ho and Hsing ...

In this article, we incorporate an autoregressive time-series framework into models for animal survival using capture-recapture data. Researchers modeling animal survival probabilities as the realization of a random process have typically considered survival to be independent from one time period to the next. This may not be realistic for some populations. Using a Gibbs sampling approach, we ca...

We simplify simplicial depth for regression and autoregressive growth processes in two directions. At first we show that often simplicial depth reduces to counting the subsets with alternating signs of the residuals. The second simplification is given by not regarding all subsets of residuals. By consideration of only special subsets of residuals, the asymptotic distributions of the simplified ...

Effects of additive noise on a series of the periods of oscillations in unidirectionally coupled ring neural networks of ring oscillator type are studied. Kinematical models of the traveling waves of an inconsistency, i.e. the successive same signs in the states of adjacent neurons in the network, are derived. A series of the half periods in the network of N neuron is then expressed by the sum ...

Spatial modelling has its applications in many fields like geology, agriculture, meteorology, geography, and so forth. In time series a class of models known as Generalised Autoregressive GAR has been introduced by Peiris 2003 that includes an index parameter δ. It has been shown that the inclusion of this additional parameter aids in modelling and forecasting many real data sets. This paper st...

In this paper, we investigated the use of seasonal autoregressive integrated moving average (SARIMA) time series models for fault detection in semiconductor etch equipment data. The derivative dynamic time warping algorithm was employed for the synchronization of data. The models were generated using a set of data from healthy runs, and the established models were compared with the experimental...

In this paper, we consider a spatial-autoregressive model with autoregressive disturbances, where we allow for endogenous regressors in addition to a spatial lag of the dependent variable. We suggest a two-step generalized method of moments (GMM) and instrumental variable (IV) estimation approach extending earlier work by, e.g., Kelejian and Prucha (1998, 1999). In contrast to those papers, we ...

This paper develops a theoretical framework for the analysis of smallsample properties of forecasts from general autoregressive models under structural breaks. Finite-sample results for the mean squared forecast error of one-step ahead forecasts are derived, both conditionally and unconditionally, and numerical results for different types of break specifications are presented. It is established...

In this paper, a method to detect unknown signals ina non-stationaryenvironmentis proposed. In addition, due to the sensor, the data are corrupted by an additive measurement stationary zero-mean white noise.Our approach, which can be useful in a wide range of situations such as the analysis of the object passing by, anomaly detection and digital communications, operates in three steps.Firstly, ...

In this paper, variable bit rate (VBR) H.261 encoded video traffic is modeled by a nonlinear time series process. A threshold autoregressive (TAR) process is of particular interest. The TAR model is comprised of a set of autoregressive (AR) processes that are switched according to the amplitude of bit rates of the time series. The TAR model is shown to capture accurately the statistical charact...