A Comparison of Some Control Chart Procedures

By S. W. Roberts. 1966

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Citations in the corpus

The Art of Evaluating Monitoring Schemes — How to Measure the Performance of Control Charts? (2006, unknown): “…0.5 1.0 1.5 2.0 2.5 3.0 689 39 10.0 5.1 3.4 2.6 2.2 L * 42 12 6.3 4.3 3.4 2.8 W.05 EWMA * 42 12 6.6 4.7 3.6 3.0 W.01 * 43 13 7.0 5.0 3.9 3.3 W.001 * 45 14 8.2 6.0 4.8 4.1 W<10−9 CUSUM 724 34 9.9 5.6 3.9 3.1 2.6 L/W GRSR 697 33 10.4 6.2 4.4 3.5 2.9 L/W Roberts (1966) A comparison of some…”
Third-order Asymptotic Optimality of the Generalized Shiryaev-Roberts Changepoint Detection Procedures (2010, methodology): “Sequential analysis, sequential changepoint detection, Shiryaev–Roberts procedure AMS subject classifications.”
Is Average Run Length to False Alarm Always an Informative Criterion? (2008, unknown): “Page (1954), Roberts (1966) , Shiryayev (1963, 1978), Lorden (1971), Moustakides (1986),”
On optimality of the Shiryaev-Roberts procedure for detecting a change in distribution (2009, methodology): “…Problems, Shiryaev-Roberts Procedures, Sequential Detection 1 In 1961, for detecting a change in the drift of a Brownian motion, Shiryaev introduced a change detection procedure, which is now usually referred to as the Shiryaev-Roberts (SR) procedure (Shiryaev, 1961, 1963 and Roberts, 1966).”
EARLY DETECTION OF A CHANGE IN POISSON RATE AFTER ACCOUNTING FOR POPULATION SIZE EFFECTS (2011, background): “To explain this, a further asymptotic optimality analysis is developed under a new asymptotic setting that is more suitable to our finite-sample numerical simulations.”
ON OPTIMALITY PROPERTIES OF THE SHIRYAEV-ROBERTS PROCEDURE (2007, background): “, Tartakovsky (1991), Tartakovsky and Veeravalli (2004) and Tartakovsky, Rozovskii, Blažek and Kim (2006). The practical implication of Theorem 2 is that in these circumstances one has reason to prefer the Shiryaev-Roberts procedure to other surveillance schemes.”
Sequential change-point detection when unknown parameters are present in the pre-change distribution (2006, unknown): “The Annals of Statistics 2006, Vol. 34, No. 1, 92–122 DOI: 10.1214/009053605000000859 © Institute of Mathematical Statistics, 2006”
Optimal and Asymptotically Optimal CUSUM Rules for Change Point Detection in the Brownian Motion Model with Multiple Alternatives (2006, methodology): “The former is used in the comparison between Roberts’ exponentially weighted moving averages (EWMA) rule (see [11]) with Page’s cumulative sums (CUSUM) rule (see [9]) and the Shiryaev–Roberts rule (see [13] and [12]) appearing in the paper by Srivastava and Wu [16] for the one-sided alternative in t”
Economic Design of EWMA Charts with Variable Sampling Intervals (2006, background): “CHAO-YU CHOU ET AL. the process target value ( Roberts, 1966 ).”
EWMA Control Charts for Monitoring Optimal Portfolio Weights (2007, background): “The alternative criteria of Roberts (1966), Lorden (1971), and Pollak and Siegmund (1975) are not considered here.”
Some Current Directions in the Theory and Application of Statistical Process Monitoring (2014, methodology): “The Shiryaev–Roberts surveillance method (Shiryaev (1963), Roberts (1966)) has a quasi-Bayesian justification.”
Data-efficient minimax quickest change detection (2012, methodology): “Setting ρ = 0 in the expression for Rn,ρ we get the Shiryaev-Roberts (SR) statistic [21]:”
Efficient scalable schemes for monitoring a large number of data streams (2010, methodology): “…local monitoring schemes are available in the literature, and a partial list includes Shewhart’s control chart; moving average charts; Page’s cumulative sum, or CUSUM, procedure; and the Shiryaev–Roberts procedure; see, for example, Shewhart (1931), Page (1954), Shiryaev (1963) and Roberts (1966).”
Nonanticipating estimation applied to sequential analysis and changepoint detection (2005, methodology): “In the well-known Shiryayev–Roberts detection scheme [11, 12], a change from parameter value θ = θ0 (possibly multidimensional) to θ = θ1, say, in the distribution of a sequence of i.”
Asymptotic operating characteristics of an optimal change point detection in hidden Markov models (2004, background): “Roberts (1966) considered the non-Bayesian setting, and studied by simulation the average run length of this rule, and found it to be very good.”
SEQUENTIAL ANALYSIS: SOME CLASSICAL PROBLEMS AND NEW CHALLENGES (2001, methodology): “…and moving average charts is the average run length (ARL), which is defined as E θ T when the quality parameter remains at a fixed level θ. Roberts (1966) studied by simulation the ARL of moving average schemes of the form N * = inf{n : n i=1 a n−i X i ≥ c} for the special cases a i =…”