A Shewhart control chart using a re-submission sampling scheme was recently introduced. This sampling scheme allows for the selection of up to (m-1) additional samples at any time point if the chart statistic exceeds the control limits. The process is declared to be out-of-control only if all m statistics fall beyond the control limits; otherwise it is declared to be in-control. This chart was recommended over the conventional Shewhart chart because of its better average run length (ARL) profile. Reliance solely on the ARL metric, however, fails to account for the increased number of observations that could be drawn from the process before reaching a decision. In this study, we show that the classical charts can significantly outperform the resubmission-based control chart when more appropriate performance metrics are considered.

Machine learning approaches for image classification have led to impressive advances in that field. For example, convolutional neural networks are able to achieve remarkable image classification accuracy across a wide range of applications in industry, defense, and other areas. While these machine learning models boast impressive accuracy, a related concern is how to assess and maintain calibration in the predictions these models make. A classification model is said to be well calibrated if its predicted probabilities correspond with the rates events actually occur. While there are many available methods to assess machine learning calibration and recalibrate faulty predictions, less effort has been spent on developing approaches that continually monitor predictive models for potential loss of calibration as time passes. We propose a cumulative sum-based approach with dynamic limits that enable detection of miscalibration in both traditional process monitoring and concept drift applications. This enables early detection of operational context changes that impact image classification performance in the field. The proposed chart can be used broadly in any situation where the user needs to monitor probability predictions over time for potential lapses in calibration. Importantly, our method operates on probability predictions and event outcomes and does not require under-the-hood access to the machine learning model.

Recent studies have explored the analysis of data from experimental designs using neutrosophic statistics. These studies have reported neutrosophic bounds on the statistics in analysis of variance tables. In this paper, following Woodall et al. (2025), a simple simulation-based approach is used to demonstrate that the reported neutrosophic bounds on these statistics are either incorrect or too inaccurate to be useful. We explain why the neutrosophic calculations are incorrect using two simple examples.

In recent years, many neutrosophic statistical methods have been proposed as generalizations of classical statistical methods and claimed to be superior when there is indeterminacy in the data values, the sample size, and/or the design parameters of the statistical method. We discuss several issues regarding these methods here. We argue that sample sizes and design parameter values selected by the practitioner are always known. Furthermore, the applicability and usefulness of neutrosophic statistical methods is questioned. In several examples we show that the results of neutrosophic analysis were either incorrect or not useful. We recommend a simpler and much more informative simulation-based method be used instead for the statistical analysis of interval data.

Several methods have been proposed for process monitoring with interval-valued data. We examine these methods and propose a simulation-based approach that is much easier to implement and has a much clearer interpretation. We consider both Phase I and Phase II implementation of interval data control charts and assess the loss of information from the use of interval data of various widths.

Recently, several control charts have been proposed in the statistical process monitoring literature based on a belief statistic. In this article we compare the zero-state average run-lengths and conditional expected delay profiles of the exponentially weighted moving average (EWMA) and belief statistic-based (BSB) charts. We show that an ordinary EWMA chart is far better than the BSB chart when detecting delayed shifts in the mean of a normally distributed process.

Control chart performance is often measured using average run length or median run length, which gives the expected or median number of samples to signal. It is often argued that on average, one chart will signal a process change quicker than another, and is therefore a better choice. Average and median run length do not, however, answer the question of which method will be more likely to signal first. We introduce the idea of "first to signal" and compare charts based on this criterion.

One of the tasks required of most statistics researchers and academic faculty is to publish their innovative ideas in the peer-reviewed literature. In this paper, we provide guidance about the different stages of the process as experienced authors and offer advice from those who hold the decision about the success or failure of these papers, namely the editors of applied statistics journals. The paper is organized into four sections focusing on the different stages of publishing: (1) Planning what to write about, where to submit and how to organize the paper; (2) The process of writing the paper; (3) Interpreting and responding to peer-reviews from the journal editors and referees to prepare for resubmission; and (4) General comments about the publication process, including collaboration and mentoring. Each section starts with fundamentals provided by the moderators (C.A.C. and L.L.) on key aspects to consider on each topic and then is followed with discussion from some current and past editors of impactful journals in the field of applied statistics. Our hope is that this collection of insights may help accelerate learning about the process for young researchers and help all researchers to understand some of the important often-unspoken aspects of the process.

In many recent articles authors have advocated the use of attribute charts based on the number of censored values for monitoring censored lifetime data. In this article we compare the average run-length performance of one-sided and two-sided Shewhart charts based on the conditional expected value (CEV) with the corresponding attribute charts. The overall findings indicate that using an attribute chart to monitor the mean of a Weibull lifetime probability distribution with right censoring does not perform as well as the CEV-based chart.

In this short note, we reevaluate the run-length performance of the EWMA and exponentiated EWMA (Exp-EWMA) charts using the conditional expected delay metric. It is found that the enhancements offered by the Exp-EWMA chart over the EWMA chart in the zero-state setup are marginal. Given its simplicity in implementation and its ability to encompass the functionality of the Exp-EWMA chart in detecting delayed shifts in the process mean, the EWMA chart remains the preferred choice over the Exp-EWMA chart.

In this article, we study the effect of estimation error on the conditional false alarm rate (CFAR) of the exponentially weighted moving average (EWMA) chart based on the estimated dynamic probability control limits (EDPCLs). It is found that the effect is large, but with an increase in the Phase-I sample size, the effect of estimation error may be reduced. In addition, an alternative approach is suggested for determining the EDPCLs for the EWMA chart that may help reduce the effect of estimation error on the CFAR profiles.

We clarify the use of the widely used multiple dependent state sampling (MDSS) technique and its generalization (GMDSS) with control charts. We show that these rules are simple modifications of well-known supplementary runs rules. The MDSS and GMDSS rules are inefficient; however, they do not distinguish between the upper and lower warning regions. We show that the application of the more conventional supplementary runs rules, i.e. those side-sensitive with respect to the warning regions, provides significantly better performance than the MDSS and GMDSS techniques. In addition, we show that a commonly used expression for determining the average run length of the GMDSS method is incorrect and provide a Markov chain alternative approach.

In this article, we review the literature on the use of the repetitive sampling technique with quality control charts. We raise some important concerns and questions regarding its application and underlying assumptions. We show that a fixed sample size method that requires on average the same amount of sampling can be designed to have as good or better statistical properties when monitoring with data that are normally distributed. Repetitive sampling methods can be useful, however, when monitoring with count data.

We provide an overview and discussion of some issues and guidelines related to monitoring univariate processes with control charts. We offer some advice to practitioners to help them set up control charts appropriately and use them most effectively. We propose a four-phase framework for control chart set-up, implementation, use, and maintenance. In addition, our recommendations may be useful for researchers in the field of statistical process monitoring. We identify some current best practices, some misconceptions, and some practical issues that rely on practitioner judgment.

We demonstrate that the recently introduced triple generally weighted moving average (GWMA) chart and its counterpart, the double GWMA chart, incorporate sub-optimal weighting patterns that may assign more weight to certain historical data points at the expense of more recent ones. Moreover, these control charts, when compared to the exponentially weighted moving average (EWMA) chart, exhibit a substantial computational burden. Our findings underscore that a well-designed EWMA chart offers superior overall performance in comparison to these control charts.

Nearly one hundred types of control charts have been proposed that incorporate ranked-set sampling (RSS) methods. The performance of these charts has been evaluated with comparisons to existing charts based on simple random sampling. The reduction of the standard error in estimating the parameter being monitored with RSS leads to uniformly better average run length performance. We show, however, that these performance comparisons can be very misleading once the sampling strategy over time is considered more carefully with the benefits of RSS being considerably overstated. We consider the most basic RSS method when monitoring the mean of the process, but the approach can be applied to evaluate other RSS monitoring methods.

Industry 4.0 has emerged as an important era for process monitoring and improvement. Our expository paper provides a historical perspective on research and practice of statistical process monitoring (SPM) from the 1920s to the present to bring a high-level view of current practice and research directions. We focus on the Industry 4.0 era, which began around 2011 with the introduction of cyber-physical systems and the growth of the Internet of Things. These technological changes have brought tremendous challenges and opportunities to SPM that can only be met with new paradigms for the problems we aim to solve and the approaches we use to evaluate SPM methodology. We provide our perspective on these challenges, primarily focusing on industrial applications. We give recommendations on the evaluation and comparison of monitoring methods to improve the usefulness of research in this area.

We show that an auxiliary information based (AIB) cumulative sum (CUSUM) chart is equivalent to a special case of the multivariate CUSUM control chart proposed by J. D. Healy. Due to this equivalence, the limitations and restrictions of the AIB-CUSUM chart also apply to Healy's multivariate CUSUM chart.

We review the rapidly growing literature on auxiliary information-based (AIB) process monitoring methods. Under this approach, there is an assumption that the auxiliary variable, which is correlated with the quality variable of interest, has a known mean, or some other parameter, which cannot change over time. We demonstrate that violations of this assumption can have serious adverse effects both when the process is stable and when there has been a process shift. Some process shifts can become undetectable. We also show that the basic AIB approach is a special case of simple linear regression profile monitoring. The AIB charting techniques require strong assumptions. Based on our results, we warn against the use of AIB approach in quality control applications.