Results 221 to 230 of about 30,999 (254)

Local Estimation of Spatial Autocorrelation Processes

2009
The difficulties caused by the lack of stability in the parameters of an econometric model are well known: biased and inconsistent estimators, misleading tests and, in general, wrong inference. Their importance explains the attention that the literature has dedicated to the problem.
Fernando López, Jesús Mur, Ana Angulo
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MONITORING AUTOCORRELATED PROCESSES

2011
Several control schemes for monitoring process mean shifts, including cumulative sum (CUSUM), weighted cumulative sum (WCUSUM), adaptive cumulative sum (ACUSUM) and exponentially weighted moving average (EWMA) control schemes, display high performance in detecting constant process mean shifts.
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The sample autocorrelation function of I(1) processes

Statistical Papers, 1994
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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ASYMPTOTIC PROPERTIES OF THE SAMPLE AUTOCORRELATIONS AND PARTIAL AUTOCORRELATIONS OF A MULTIPLICATIVE ARIMA PROCESS

Journal of Time Series Analysis, 1985
Abstract. We shall investigate the asymptotic behaviour of the sample autocorrelations and partial autocorrelations of a multiplicative ARIMA process and derive their limiting distributions. Some simulations are presented to illustrate the results obtained.
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Maximum autocorrelations for moving average processes

Biometrika, 1974
Davies, N., Pate, M. B., Frost, M. G.
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SPC for short-run multivariate autocorrelated processes

Journal of Applied Statistics, 2011
This paper discusses the development of a multivariate control charting technique for short-run autocorrelated data manufacturing environment. The proposed approach is a combination of the multivariate residual charts for autocorrelated data and the multivariate transformation technique for i.i.d. process observations of short lengths.
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An EWMA for Monitoring Stationary Autocorrelated Process

2009 International Conference on Computational Intelligence and Software Engineering, 2009
When control charts are used to monitor a process, a standard assumption is that observations from the process at different times are independent random variables. However, the independence assumption is often not reasonable for processes of interest in many applications because the dynamics of the process product autocorrelation in the process ...
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Statistical Process Control for Autocorrelated Processes: A Case-Study

1997
Statistical control charts are usually designed to monitor independently distributed observations, typically subject to a normal distribution. For many industrial processes the normal distribution may indeed provide an adequate description of data. When production is in discrete items, the assumption of independence may often be reasonable, whereas ...
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Cusum Charts for Monitoring an Autocorrelated Process

Journal of Quality Technology, 2001
Chao-Wen Lu
exaly  

Optimal filtering for systems with finite-step autocorrelated process noises, random one-step sensor delay and missing measurements

Communications in Nonlinear Science and Numerical Simulation, 2016
Dongyan Chen
exaly