Statistical process control (SPC) consists of various tools for effective monitoring of the production processes and services to ensure their stable and satisfactory performance. A control chart is an important tool of SPC for detecting the process shifts that may undermine the quality of the products or services. In the literature, a mixed exponentially weighted moving average–moving average (EWMA–MA) control chart for monitoring the process location is proposed to enhance the overall shift detection ability of the EWMA control chart. It is noted that the moving averages terms were considered as independent irrespective of their order. Consequently, the covariance terms are ignored while deriving the variance expression of the monitoring statistic. However, the successive moving averages of span w might not be independent since each term includes w − 1 preceding samples’ information. In this study, the variance expression of the mixed EWMA-MA charting statistic is derived by considering the dependency among the sequential moving averages. The control limits of the mixed EWMA-MA control chart are revised and the run-length profile is studied by using Monte Carlo simulations. The performance of the mixed EWMA-MA chart is compared with the existing counterparts and its robustness under various process distributions is studied. In the end, a real-life example is provided to demonstrate its application by using the data from a combined cycle power plant.

Expectiles have gained considerable attention in recent years due to wide applications in many areas. In this study, the k-nearest neighbours approach, together with the asymmetric least squares loss function, called ex-$kNN$, is proposed for computing expectiles. Firstly, the effect of various distance measures on ex-$kNN$ in terms of test error and computational time is evaluated. It is found that Canberra, Lorentzian, and Soergel distance measures lead to minimum test error, whereas Euclidean, Canberra, and Average of $(L_1,L_{\infty })$ lead to a low computational cost. Secondly, the performance of ex-$kNN$ is compared with existing packages er-boost and ex-svm for computing expectiles that are based on nine real life examples. Depending on the nature of data, the ex-$kNN$ showed two to 10 times better performance than er-boost and comparable performance with ex-svm regarding test error. Computationally, the ex-$kNN$ is found two to five times faster than ex-svm and much faster than er-boost, particularly, in the case of high dimensional data.

Many researchers employed Poisson distribution-based control charts to monitor count data. Nevertheless, these charts can handle count data that deviate from the Poisson assumption of equal mean and variance. This paper suggests a new control chart (CC) that uses the generalized Conway–Maxwell–Poisson (GCOMP) distribution, which can deal with count data that have different levels of dispersion and zero-inflation (ZI). The proposed chart is designed considering the total number of counts. The main advantage of this study is that it pays attention to the tails of the count data when monitoring the process. The performance is measured by the average run length using L control limits at different sample sizes and parametric settings. The findings demonstrate that, for count data with varying tail behaviors, the proposed chart performs better compared to existing CCs. ZI count data can also be monitored with the proposed chart. The proposed chart can be applied in a variety of fields, as verified by the examples provided in this paper.

The zero-inflated Conway-Maxwell Poisson (ZICOMP) distribution models count data with many zero observations. This distribution assumes that zero observations occur with probability \(\:p\), and the count of nonconformities in a product unit follows the Conway-Maxwell Poisson (COMP) distribution with parameters \(\:\mu\:\) and \(\:\gamma\:\). The ZICOMP distribution is flexible in accommodating various dispersion patterns in zero-inflated (ZI) datasets and effectively models over-dispersed, under-dispersed, or equi-dispersed data. This study provides an in-depth analysis of the ZICOMP-Shewhart control chart, as discussed by Alevizakos and Tasias¹, to assess its effectiveness in detecting shifts in the rate parameter (\(\:\mu\:\)) while assuming constant dispersion and zero-inflation parameters. The key focus of this research is the selection of the limit coefficient to achieve the desired in-control (IC) average run length and assessing Type II error sensitivity for improved out-of-control (OOC) detection. Through extensive simulations, we examine the comparative efficiency of the ZICOMP-Shewhart chart against the traditional Shewhart chart under the COMP distribution. Additionally, the effectiveness of the ZICOMP-Shewhart control chart is demonstrated through a real-life example.

The article presents flood frequency modeling of the Sutlej River’s gauging sites located in Pakistan. The objective is to search for the most cogent mix of probability distribution and parameter estimation approach for each site separately. Different probability distributions that have been recommended in the literature are fitted for modeling maximum annual water discharge. Separately for each site, the parameters of the competing probability distributions are estimated through different estimation strategies. Different compounds of distributions and estimation methods are compared for each site through seven different performance metrics, and the most plausible compound is recommended for that particular gauging station. Results from ranking different models revealed that the generalized Pareto distribution estimated through the weighted least squares method is most appropriate for both sites considered for analysis. Finally, based on the selected blend of distribution and estimation method, an estimate of annual maximum flow, along with 95% parametric bootstrap confidence intervals, is provided for different return years.