Procedures for Reacting to a Change in Distribution

Lorden, G.

doi:10.1214/aoms/1177693055

Cited by 1,164 publications

(957 citation statements)

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“…These include the CuSum ("cumulative sum") procedure of Page [Pag54] and Lorden [Lor71], the Bayesian procedure of Shiryayev [Shi63,Shi73] and Roberts [Rob66], and the EWMA ("exponentially weighted moving average") procedure of Roberts [Rob59].…”

Section: Spc Background and Literaturementioning

confidence: 99%

Evaluating Statistical Methods for Syndromic Surveillance

Stoto

Fricker

Jain

et al. 2006

Statistical Methods in Counterterrorism

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Since the terrorist attacks on September 11, 2001, many state and local health departments around the United States have started to develop syndromic surveillance systems. Syndromic surveillance -a new concept in epidemiology -is the statistical analyses of data on individuals seeking care in emergency rooms (ER) or other health care settings with preidentified sets of symptoms thought to be related to the precursors of diseases. Making use of existing health care or other data, often already in electronic form, these systems are intended to give early warnings of bioterrorist attacks or other emerging health conditions. By focusing on symptoms rather than confirmed diagnoses, syndromic surveillance aims to detect bioevents earlier than would be possible with traditional surveillance systems. Because potential bioterrorist agents such as anthrax, plague, brucellosis, tularemia, Q-fever, glanders, smallpox, and viral hemorrhagic fevers initially exhibit symptoms ("present" 142 Michael A. Stoto et al. in medical terminology) of a flulike illness, data suggesting a sudden increase of individuals with fever, headache, muscle pain, and malaise might be the first indication of a bioterrorist attack or natural disease outbreak. Syndromic surveillance is also thought to be useful for early detection of natural disease outbreaks [Hen04].Research groups based at universities, health departments, private firms, and other organizations have proposed and are developing and promoting a variety of surveillance systems purported to meet public health needs. These include methods for analysis of data from healthcare facilities, as well as reports to health departments of unusual cases. Many of these methods involve intensive, automated statistical analysis of large amounts of data and intensive use of informatics techniques to gather data for analysis and to communicate among physicians and public health officials [WTE01]. Some of these systems go beyond health care data to include nonhealth data such as over-the-counter (OTC) pharmaceutical sales and absenteeism that might indicate people with symptoms who have not sought health care [Hen04].There are a number of technological, logistical, and legal constraints to obtaining appropriate data and effective operation of syndromic surveillance systems [Bue04]. However, even with access to the requisite data and perfect organizational coordination and cooperation, the statistical challenges in reliably and accurately detecting a bioevent are formidable. The object of these surveillance systems, of course, is to analyze a stream of data in realtime and determine whether there is an anomaly suggesting that an incident has occurred. All data streams, however, have some degree of natural variability. These include seasonal or weekly patterns, a flu season that appears at a different time each winter or perhaps not at all, differences in coding practices, sales promotions for OTC medications, and random fluctuations due to small numbers of individuals with particular symptoms. Fur...

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Section: Spc Background and Literaturementioning

confidence: 99%

Evaluating Statistical Methods for Syndromic Surveillance

Stoto

Fricker

Jain

et al. 2006

Statistical Methods in Counterterrorism

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show abstract

“…Lorden, 1971), in studies of CUSUM chart to improve the nominal performance. Our algorithm is also applicable to compute ARL of CUSUM with FIR feature, by using Hc(s) and Lc(s) instead of Hc(0) and Lc(0).…”

Section: Final Commentsmentioning

confidence: 99%

Design of Variance CUSUM

Lee¹,

Hong²,

Lee³

2009

Korean Journal of Applied Statistics

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We suggest a fast and accurate algorithm to compute ARLs of CUSUM chart for controling process variance.The algorithm solves the characteristic integral equations of CUSUM chart (for controling variance). The algorithm is directly applicable for the cases of odd sample sizes. When the sample size is even, by using well-known approximation algorithm combinedly with the new algorithm for neighboring odd sample sizes, we can also evaluate the ARLs of CUSUM charts efficiently and accurately. Based on the new algorithm, we consider the optimal design of upward and downward CUSUM charts for controling process variance.

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“…The CUSUM chart was initially presented by Page [1]. It has been shown that CUSUM chart is asymptotically optimal under minimax type criteria (see, e.g., Lorden [2]). The EWMA chart was initially introduced by Roberts [3].…”

Section: Introductionmentioning

confidence: 99%

Optimal Parameters of Moving Average Control Chart

Areepong¹

2012

IJAPM

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Abstract-The objective of this paper is to study Statistical Process Control (SPC) with a Moving Average control chart (MA) for monitoring the non-conforming product. The characteristic of control chart is Average Run Length (ARL) which is the average number of samples taken before an action signal is given. The ARL should be sufficiently large while the process is still in-control and the Average Delay time (AD) (mean delay of true alarm times) should be small when the process goes out-of-control. The explicit formulae of ARL and AD for MA-based are presented when observations are from binomial distribution. In particular, the explicit analytical formulas for evaluating ARL and AD be able to get a set of optimal parameters which depend on a width of the moving average ( w ) and width of control limit ( H ) for designing MA chart with minimum of AD.

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Procedures for Reacting to a Change in Distribution

Cited by 1,164 publications

References 10 publications

Evaluating Statistical Methods for Syndromic Surveillance

Evaluating Statistical Methods for Syndromic Surveillance

Design of Variance CUSUM

Optimal Parameters of Moving Average Control Chart

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