This paper develops a new multivariate control charting method for vector autocorrelated and serially correlated processes. The main idea is to propose a Bayesian multivariate local level model, which is a generalization of the Shewhart-Deming model for autocorrelated processes, in order to provide the predictive error distribution of the process and then to apply a univariate modified EWMA con...

Abundance trends are the basis for many classifications of threat and recovery status, but they can be a challenge to interpret because of observation error, stochastic variation in abundance (process noise) and temporal autocorrelation in that process noise. To measure the frequency of incorrectly detecting a decline (false-positive or false alarm) and failing to detect a true decline (false-n...

This paper compares two alternative models for autocorrelated count time series. The first model can be viewed as a ‘single source of error’ discrete state space model, in which a time-varying parameter is specified as a function of lagged counts, with no additional source of error introduced. The second model is the more conventional ‘dual source of error’ discrete state space model, in which ...

A Cumulative Sum (CUSUM) control chart capable of detecting changes in both the mean and the standard deviation for autocorrelated data, referred to as the Max-CUSUM chart for Autocorrelated Process chart (MCAP chart), is proposed. This chart is based on fitting a time series model to the data, and then calculating the residuals. The observations are represented as a first-order autoregressive ...

This paper considers a single equation cointegrating model and proposes the locally best invariant and unbiased (LBIU) test for the null hypothesis of cointegration. We derive the asymptotic local power functions and compare them with the standard residualbased test, and we show that the LBIU test is more powerful in a wide range of local alternatives. Then, we conduct a Monte Carlo simulation ...

The realized variance (RV) is known to be biased because intraday returns are contaminated with market microstructure noise, in particular if intraday returns are sampled at high frequencies. In this paper, we characterize the bias under a general specification for the market microstructure noise, where the noise may be autocorrelated and need not be independent of the latent price process. Wit...

Recently, statistical process control (SPC) methodologies have been developed to accommodate autocorrelated data. To construct control charts for stationary process data, the process variance needs to be estimated. For an independently identically distributed sequence of a random variable, the variance is usually estimated by the sample variance. For a weakly stationary process, different estim...

Outliers and influential observations have important effects on the regression analysis. The goal of this paper is to extend the mean-shift model for detecting outliers in case of ridge regression model in the presence of stochastic linear restrictions when the error terms follow by an autoregressive AR(1) process. Furthermore, extensions of measures for diagnosing influential observations are ...