CONTINUOUS INSPECTION SCHEMES

By E. S. Page. 1954

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Differentially Private Change-Point Detection (2018, methodology): “Change-point detection is a canonical problem in statistics that has been studied for nearly a century; selected results include [She31, Pag54, Shi63, Rob66, Lor71, Pol85, Pol87, Mou86, Lai95, Lai01, Kul01, Mei06, Mei08, Mei10, Cha17].”
Bayesian Online Changepoint Detection (2007, methodology): “Frequentist approaches to changepoint detection, from the pioneering work of Page [22, 23] and Lorden [19] to recent work using support vector machines [10], offer online changepoint detectors.”
Quickest detection of drift change for Brownian motion in generalized Bayesian and minimax settings (2006, methodology): “For discrete time, criterion (5.1) was introduced by Lorden [10] who proved that the so-called CUSUM method of Page [12] is asymptotically optimal for this criterion when T → ∞.”
Extensions of some classical methods in change point analysis (2014, methodology): “Page (1954, 1955) suggested a very simple method to test the stability of the quantiles of underlying observations.”
Detecting gradual changes in locally stationary processes (2013, methodology): “The detection of sudden structural breaks has a long history originating from statistical inference in quality control [see for example Page (1954, 1955) for some early references].”
Ensemble learning for data stream analysis: A survey (2017, methodology): “The cumulative sum approach (CUSUM) [138] detects a change of a given parameter value of a probability distribution and indicates when the change is signiﬁcant.”
SEQUENTIAL CHANGE DETECTION REVISITED (2008, methodology): “As it was demonstrated in Moustakides (1986) and Ritov (1990), solving the optimization problem in (3.3) for Lorden’s criterion and for i.i.d. observations, gives rise to the CUSUM test proposed by Page (1954).”
The Use of Control Charts in Health-Care and Public-Health Surveillance (2006, methodology): “In health-care surveillance and public-health monitoring, the arguments for the use of CUSUM charts based on the likelihood ratio approach of Page (1954) to detect sustained changes in Phase II are far more convincing than those for the use of the RSPRT based on error probabilities and Wald’s…”
Multivariate Extensions to Cumulative Sum Control Charts (2004, unknown): “l = t ∑ i=1 ln f (yi, μ0 + δ) f (yi, μ0) = − 1 2σ 2 t ∑ i=1 (yi − μ0 − δ)2 − (yi − μ0)(2) = δ σ t ∑ i=1 σ−1 ( yi − μ0 − δ 2 ) (1)”
Is Average Run Length to False Alarm Always an Informative Criterion? (2008, methodology): “In view of the history of sequential changepoint detection problems for independent observations, which were studied by Shewhart (1931) and Page (1954) but not rigorously formulated and solved until between 1963 and 1990 (Shiryayev, 1963; Lorden, 1971; Moustakides, 1986; Pollak, 1985, 1987; Ritov,…”
Fast Initial Response for CUSUM Quality-Control Schemes: Give Your CUSUM A Head Start (2000, methodology): “461.1 142.7 48.98 9.260 3.447 2.012 1.473 1.091 1.009 NOTE: PARAMETERS: H-Decision interval for the CUSUM scheme; K-reference value; S(O)-headstart value Tables 2 and 3, while Van Dobben de Bruyn (1968) and Goel and Wu (197 1) give ARL’s for one-sided schemes using a somewhat different format.”
The computation of average run length and average time to signal: an overview (2014, methodology): “Several charting schemes have been proposed to try to simplify the process of SPC, in particular the Shewhart ?̄? control chart ([1]), the CUSUM control chart ([2]) and the EWMA control chart ([3]).”
Inference for single and multiple change‐points in time series (2013, unknown): “The change-point problem was introduced by Page (1954, 1955) for the detection of change in the mean of independently and normally distributed time series.”
Should Observations Be Grouped for Effective Process Monitoring? (2004, methodology): “Even though a sample of four observations is very likely to detect the shift in only one sample, the fact that the shift will occur somewhere in the 4-hour interval between samples implies that the expected detection time is at least 2 hours. Thus, if good performance over a wide range of sustained ”