Error propagation modeling for terrain analysis can provide insights into the robustness of terrain derivatives. For unconstrained terrain derivatives, such as stream and watershed delineation, the most widely used technique for error propagation modeling employs a Monte Carlo simulation of a spatially autocorrelated error model. The current study seeks to make this methodology more accessible ...

[1] The accurate specification of observing and/or modeling error statistics presents a remaining challenge to the successful implementation of many land data assimilation systems. Recent work has developed adaptive filtering approaches that address this issue. However, such approaches possess a number of known weaknesses, including a required assumption of serially uncorrelated error in assimi...

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...

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 (falsene...

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 ...

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...