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Cumulative sum (CUSUM) and change-point analysis (CPA) are two well-established statistical process control methods to detect changes in a sequence. Both have been used in psychometric research to detect aberrant responses in a response sequence, e.g., test speededness, inattentiveness, or cheating. However, the pros and cons of CUSUM and CPA in different testing settings still remain unclear. In this paper, we conduct a comprehensive comparison of the performance of twelve CUSUM-based statistics and three CPA-based procedures in detecting test speededness. Two speededness mechanisms are considered, namely the graduate change model (GCM) and the hybrid model (HM), to test the robustness and flexibility of the two methods. Simulation studies show that the performances of the statistics are affected by the underlying data generating model, the severity of speededness, and the test length. Generally, under HM some CUSUM statistics perform much better than the CPA-based statistics. Under the GCM, the performance of the CPA statistics is dramatically improved. Taken together, due to the unknown mechanism of speededness in real applications, two CUSUM-based statistics are recommended when the test length is long (e.g., 80 items), regardless of the underlying mechanism being HM or GCM. In a relatively short (e.g., 40 items) or medium-length (e.g., 60 items) test, no statistic always ends up in the top three under both HM and GCM. In those cases, either one of the two CUSUM-based statistics mentioned above can be a reasonable choice because of their good (though not necessarily the best) performance in a wide range of conditions. [ABSTRACT FROM AUTHOR]

In this research, we propose the new mixed control chart called the mixed Moving Average-Cumulative Sum (MA-CUSUM) control chart used for monitoring parameter changes in asymmetrical and symmetrical processes. Its efficiency was compared with that of the Shewhart, Cumulative Sum (CUSUM), Moving Average (MA), mixed Cumulative Sum-Moving Average (CUSUM-MA) and mixed Moving Average-Cumulative Sum (MA-CUSUM) control charts by using their average run lengths (ARLs), the standard deviation of the run length (SDRL), and median run length (MRL) via the Monte Carlo simulation (MC). The simulation results show that the MA-CUSUM control chart was more efficient than the other control charts for small-to-moderate parameter changes for all distributions tested. To compare their applicability to real-world situations, the control charts were applied to data for the River Nile flow from 1871–1930 and mine explosions in the UK from 1875–1951. It was found that the MACUSUM control chart could more quickly detect parameter changes than the other control charts. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new methodology based on the combination of cumulative sum procedure and generalized likelihood ratio statistic for joint monitoring of the process location and dispersion. Then, we explore the effect of measurement errors on detecting ability of the proposed control chart when (i) the variance of measurement error is constant (ii) the variance of measurement error increases linearly as the level of the process mean increases. We also utilize multiple measurements on each sample point in order to decrease the adverse effects of measurement errors on the performance of the proposed control charts. Two numerical examples based on simulation studies are given to evaluate the ability of the proposed methods in terms of average run length, median run length, standard deviation of run length, and the first and third quantile points of the run length distribution ( Q1 and Q3). Finally, a real life example is given to illustrate the application of the proposed method. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Supersaturated designs are designs in which the number of factors exceeds the run size; consequently, there are not enough degrees of freedom to estimate all the main effects. The goal here is to identify the dominant factors that constitute a small proportion of the overall set of factors, according to the assumption of effect sparsity. The analysis of such designs constitutes a challenging task and, even though many methods have been proposed in the literature assuming a normal response, only few works attempted to address the case of non-normal responses. In this paper, we propose a method for screening out the most important features in supersaturated designs assuming a Bernoulli distributed response. This new approach is based on an effective chart in Statistical Process Control, the cumulative sum control chart, combined with an information theoretic measure, and it is referred as the MIC algorithm. We judge the value of MIC through comparisons with three existing approaches suggested in the literature: the least absolute shrinkage and selection operator penalization method, and two feature selection algorithms, the Conditional Mutual Information Maximization and the minimal-redundancy-maximal-relevance. The simulation study reveals that the proposed method can be considered an advantageous method because of its extremely good performance in terms of statistical power. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The cumulative sum (CUSUM) and the exponentially weighted moving average (EWMA) control charts are alternatives to the Xbar chart. The CUSUM's theoretical optimality suggests that it should outperform the EWMA for detecting persistent shifts, but practitioners have long thought that the two perform about equally. Each also involves design decisions on the likely shift in the process. This article quantifies the effect of these choices and concludes that, though the CUSUM outperforms the EWMA at the shift for which each was designed, if the actual shift is smaller than that used in the design, the EWMA may respond faster. [ABSTRACT FROM AUTHOR]

Cumulative sum control charts (CUSUM) are constructed for controlling the parameters of the binomial distribution when the underlying distribution is Poisson. It is observed that the parameters of the V-mask and the Average Run Length (ARL) change considerably for a slight shift in the parameters (or ratio) of the distribution understudy. [ABSTRACT FROM AUTHOR]