On the run length of a Shewhart chart for correlated data

W. Schmid

doi:10.1007/bf02926025

Run Length Distribution and Percentiles: The Shewhart Chart with Unknown Parameters

Quality Engineering ◽

10.1080/08982110701276653 ◽

2007 ◽

Vol 19 (2) ◽

pp. 119-127 ◽

Cited By ~ 53

Author(s):

S. Chakraborti

Keyword(s):

Length Distribution ◽

Unknown Parameters ◽

Run Length ◽

Shewhart Chart ◽

Run Length Distribution

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The Exact Run Length Distribution and Design of the Shewhart Chart with Estimated Parameters Based on Median Run Length

Communications in Statistics - Simulation and Computation ◽

10.1080/03610918.2014.889158 ◽

2016 ◽

Vol 45 (6) ◽

pp. 2081-2103 ◽

Cited By ~ 14

Author(s):

W. L. Teoh ◽

Michael B. C. Khoo ◽

Philippe Castagliola ◽

M. H. Lee

Keyword(s):

Length Distribution ◽

Run Length ◽

Shewhart Chart ◽

Estimated Parameters ◽

Run Length Distribution

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Time Series Analysis and Statistical Process Control for Anomaly Detection: An Application to the Situation of PM 10 in Northern Thailand

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.866.379 ◽

2017 ◽

Vol 866 ◽

pp. 379-382

Author(s):

Unchalee Tonggumnead ◽

Kittipong Klinjan

Keyword(s):

Process Control ◽

Statistical Process Control ◽

Moving Average ◽

Correlated Data ◽

Cusum Chart ◽

Statistical Process ◽

Shewhart Chart ◽

Ewma Chart ◽

Pm 10 ◽

Weighted Moving Average

The monitoring of processes is a vital mechanism for ensuring that such processes remain safe and under control. The present research aims to solve problems associated with correlated data by applying the Box-Jenkins method integrated with statistical process control (SPC) tools, namely the Shewhart chart, the moving average chart, the cumulative sum (CUSUM) chart, and the exponentially weighted moving-average (EWMA) chart. The efficiency of the four SPC tools was also compared in terms of the false alarm rate (FAR) and the missed detection rate (MDR). The findings indicated that the EWMA chart was the most effective in detecting anomaly, the Shewhart chart and the moving average chart produced high MDR, and the CUSUM chart suffered the highest FAR.

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Quadratic Discriminant Analysis of Spatially Correlated Data

Nonlinear Analysis Modelling and Control ◽

10.15388/na.2001.6.1.15212 ◽

2001 ◽

Vol 6 (2) ◽

pp. 15-28 ◽

Cited By ~ 2

Author(s):

K. Dučinskas ◽

J. Šaltytė

Keyword(s):

Discriminant Function ◽

Error Rate ◽

Gaussian Random Field ◽

Correlated Data ◽

Covariance Matrices ◽

Classification Rule ◽

Spatial Correlations ◽

Linear Quadratic ◽

Spatial Correlation Function ◽

Spatially Correlated

The problem of classification of the realisation of the stationary univariate Gaussian random field into one of two populations with different means and different factorised covariance matrices is considered. In such a case optimal classification rule in the sense of minimum probability of misclassification is associated with non-linear (quadratic) discriminant function. Unknown means and the covariance matrices of the feature vector components are estimated from spatially correlated training samples using the maximum likelihood approach and assuming spatial correlations to be known. Explicit formula of Bayes error rate and the first-order asymptotic expansion of the expected error rate associated with quadratic plug-in discriminant function are presented. A set of numerical calculations for the spherical spatial correlation function is performed and two different spatial sampling designs are compared.

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A Novel Meteosat Second Generation Image Compression Method Based on Radon Transform, Linear Predictive Coding with Filtering and Sorted Run Length Coding

International Review on Computers and Software (IRECOS) ◽

10.15866/irecos.v10i4.5704 ◽

2015 ◽

Vol 10 (4) ◽

pp. 438 ◽

Cited By ~ 1

Author(s):

Mehdi Cherifi ◽

Mourad Lahdir ◽

Soltane Ameur

Keyword(s):

Image Compression ◽

Radon Transform ◽

Second Generation ◽

Predictive Coding ◽

Compression Method ◽

Linear Predictive Coding ◽

Run Length ◽

Run Length Coding

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Characterization of Pancreas at Diabetic Patients in CT images using Texture Analysis

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i7.88 ◽

2017 ◽

Vol 7 (7) ◽

pp. 8

Author(s):

Mona E. Elbashier ◽

Suhaib Alameen ◽

Caroline Edward Ayad ◽

Mohamed E. M. Gar-Elnabi

Keyword(s):

Size Distribution ◽

Texture Analysis ◽

Classification Accuracy ◽

Ct Images ◽

Diabetic Patients ◽

Gray Level ◽

Grey Level ◽

Run Length ◽

Interactive Data

This study concern to characterize the pancreas areato head, body and tail using Gray Level Run Length Matrix (GLRLM) and extract classification features from CT images. The GLRLM techniques included eleven’s features. To find the gray level distribution in CT images it complements the GLRLM features extracted from CT images with runs of gray level in pixels and estimate the size distribution of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level distribution of images. The results show that the Gray Level Run Length Matrix and features give classification accuracy of pancreashead 89.2%, body 93.6 and the tail classification accuracy 93.5%. The overall classification accuracy of pancreas area 92.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate pancreas area names.

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