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Assessing the estimation of nearly singular covariance matrices for modeling spatial variables
(Wiley, 2023)
© 2023, Institute of Mathematical Statistics. All rights reserved.Spatial analysis commonly relies on the estimation of a co-variance matrix associated with a random field. This estimation strongly impacts the prediction ...
Covariance matrix formula for Birnbaum-Saunders regression models
(TAYLOR & FRANCIS LTD, 2011)
The Birnbaum-Saunders regression model is commonly used in reliability studies. We derive a simple matrix formula for second-order covariances of maximum-likelihood estimators in this class of models. The formula is quite ...
The Synthetic Control Chart Based on Two Sample Variances for Monitoring the Covariance Matrix
(John Wiley & Sons Ltd, 2009-07-01)
In this article, we propose a new statistic to control the covariance matrix of bivariate processes. This new statistic is based on the sample variances of the two quality characteristics, in short VMAX statistic. The ...
A new chart based on sample variances for monitoring the covariance matrix of multivariate processes
(Springer London Ltd, 2009-04-01)
In this article, we propose a control chart for detecting shifts in the covariance matrix of a multivariate process. The monitoring statistic is based on the standardized sample variance of p quality characteristics we ...
A control chart based on sample ranges for monitoring the covariance matrix of the multivariate processes
(Routledge Journals, Taylor & Francis Ltd, 2011-01-01)
For the univariate case, the R chart and the S(2) chart are the most common charts used for monitoring the process dispersion. With the usual sample size of 4 and 5, the R chart is slightly inferior to the S(2) chart in ...
Control charts for monitoring the mean vector and the covariance matrix of bivariate processes
(Springer London Ltd, 2009-12-01)
In this article, we propose new control charts for monitoring the mean vector and the covariance matrix of bivariate processes. The traditional tools used for this purpose are the T (2) and the |S| charts. However, these ...
A single chart with supplementary runs rules for monitoring the mean vector and the covariance matrix of multivariate processes
(2013-08-15)
The MRMAX chart is a single chart based on the standardized sample means and sample ranges for monitoring the mean vector and the covariance matrix of multivariate processes. User's familiarity with the computation of these ...
A new chart based on sample variances for monitoring the covariance matrix of multivariate processes
(Springer London Ltd, 2009-04-01)
In this article, we propose a control chart for detecting shifts in the covariance matrix of a multivariate process. The monitoring statistic is based on the standardized sample variance of p quality characteristics we ...
The Synthetic Control Chart Based on Two Sample Variances for Monitoring the Covariance Matrix
(John Wiley & Sons Ltd, 2009-07-01)
In this article, we propose a new statistic to control the covariance matrix of bivariate processes. This new statistic is based on the sample variances of the two quality characteristics, in short VMAX statistic. The ...
A control chart based on sample ranges for monitoring the covariance matrix of the multivariate processes
(Routledge Journals, Taylor & Francis Ltd, 2011-01-01)
For the univariate case, the R chart and the S(2) chart are the most common charts used for monitoring the process dispersion. With the usual sample size of 4 and 5, the R chart is slightly inferior to the S(2) chart in ...