Showing 3 results for Mokhtari
Nasrollah Iranpanah, Morteza Mokhtari Moghadam,
Volume 10, Issue 1 (__1334382579.pdf 2010)
Abstract
Shewhart control charts are widely accepted as standard tools monitoring manufacturing statistical processes. The control charts have not applied, when the process distribution is not normal. The bootstrap is one of the resampling methods that can be used in statistical quality control without normality assumption. In most of papers, only the percentile bootstrap confidence interval is used for control limits. In this paper, we apply percentile bootstrap, bootstrap-t, bias corrected accelerated (BCa) and approximate bootstrap confidence interval (ABC) for mean control limits of statistical process. Then, the bootstrap confidence intervals are used and compared for mean control limits in simulation study. Finally, the bootstrap control limits are used for mean of CO2 data in Isfahan Zamzam factory.
S. A. Alavi, M. R. Ghasemi, M. Mokhtari, A. R. Gelalzadeh, Reza Alipoor,
Volume 12, Issue 2 (11-2012)
Abstract
The Pazanan oil field is located in Dezful embayment, 150 km south east of Ahvaz and south east of Aghajari oil field. Aghajari formation has formed surface outcrop and also the Asmari formation with 7 reservoir layers is the main reservoir rock in this oil field. In this research high fractured areas in the Pazanan oil field have been analyzed based on subsurface date and utilization of subsurface analyzes method. It seem to be the Pazanan oil anticline is an asymmetric fold with high dip in south west limb and the middle parts have been distinguished as areas with potential of fractures development with respect to longitudinal Curvature. Geometrics analysis of this structure indicate that south west limb in more parts and north east limb in middle parts have been distinguished as areas with high fractures density. Axial bending (longitudinal) of the Pazanan anticline is because of growth and propagation and combining of separate anticlines shear zone result of strike slip faults motion and old strike slip structures.
Atefe Mokhtari Hasanabadi, Manouchehr Kheradmandnia,
Volume 13, Issue 3 (11-2013)
Abstract
Monitoring process mean and variance simultaneously in a single control chart simplifies
the process monitoring. If in addition, a simultaneous control chart is capable of
recognizing the source of contamination, this capability leads to additional simplicity.
These are the reasons why simultaneous control charts have attracted many researchers and
manufacturers.
Recently, in the statistical process control literature some control charts have been
introduced which are based on the idea of Bayesian predictive density. This type of control
charts, not only brings into account the uncertainty concerning the estimation of unknown
parameters, but also do not need extensive simulations for computation of control limits.
These control charts have been introduced for mean and variance in both univariate and
multivariate situations.
Up to now, no simultaneous control chart has been introduced based on Bayesian predictive
density. In this paper, using the idea of Bayesian predictive density, we introduce a new
simultaneous control chart for monitoring univariate mean and variance. We illustrate the
important capabilities of this new chart through simulated data.
This new chart is applicable when parameters are unknown. In other words, it brings into
account the uncertainty concerning the unknown parameters. This chart is able to recognize
the source of contamination and is sensitive to small changes in the mean and variance. In
this chart the control limits, needless of simulation, can simply be obtained from normal
table.
