Hossein Masoumi Karakani

Bio: Hossein Masoumi Karakani is an academic researcher from University of Pretoria. The author has contributed to research in topics: Bayes factor & Moving average. The author has an hindex of 2, co-authored 2 publications receiving 13 citations.

A double generally weighted moving average exceedance control chart

Abstract: Since the inception of control charts by W. A. Shewhart in the 1920s they have been increasingly applied in various fields. The recent literature witnessed the development of a number of nonparametric (distribution-free) charts as they provide a robust and efficient alternative when there is a lack of knowledge about the underlying process distribution. In order to monitor the process location, information regarding the in-control process median is typically required. However, in practice this information might not be available due to various reasons. To this end, a generalized type of nonparametric time-weighted control chart labelled as the Double Generally Weighted Moving Average (DGWMA) based on the exceedance statistic (EX) is proposed. The DGWMA-EX chart includes many of the wellknown existing time-weighted control charts as special or limiting cases for detecting a shift in the unknown location parameter of a continuous distribution. The DGWMA-EX chart combines the better shift detection properties of a DGWMA chart with the robust in-control performance of a nonparametric chart, by using all the information from the start until the most recent sample to decide if a process is in-control (IC) or out-of-control (OOC). An extensive simulation study reveals that the proposed DGWMA-EX chart, in many cases, outperforms its counterparts.

Bayesian Analysis of AR(1) Model

Abstract: The first-order autoregressive process, AR (1), has been widely used and implemented in time series analysis. Different estimation methods have been employed in order to estimate the autoregressive parameter. This article focuses on subjective Bayesian estimation as opposed to objective Bayesian estimation and frequentist procedures. The truncated normal distribution is considered as a prior, to impose stationarity. The posterior distribution as well as the Bayes estimator are derived. A comparative study between the newly derived estimator and other existing estimation methods (frequentist) is employed in terms of simulation and real data. Furthermore, a posterior sensitivity analysis is performed based on four different priors; g prior, natural conjugate prior, Jeffreys' prior and truncated normal prior and the performance is compared in terms of Highest Posterior Density Region criterion.

Generally weighted moving average monitoring schemes: Overview and perspectives

Abstract: An overview of monitoring schemes from a class called generally weighted moving average (GWMA) is provided. A GWMA scheme is an extended version of the exponentially weighted moving average (EWMA) scheme with an additional adjustment parameter that introduces more flexibility in the GWMA model as it adjusts the kurtosis of the weighting function so that the GWMA scheme can be designed such that it has an advantage over the corresponding EWMA scheme in the detection of certain shift values efficiently. The parametric and distribution-free GWMA schemes to monitor various quality characteristics and its existing enhanced versions (i.e. double GWMA, composite Shewhart-GWMA, mixed GWMA-CUSUM and mixed CUSUM-GWMA) have better performance than their corresponding EWMA counterparts in many situations; hence, all such existing research works discussing GWMA-related schemes (i.e. 61 publications in total) are documented and categorized in such a manner that it is easy to identify research gaps. Finally, a number of possible future research ideas are provided.

A critique of a variety of “memory-based” process monitoring methods

Abstract: Many extensions and modifications have been made to standard process monitoring methods such as the exponentially weighted moving average (EWMA) chart and the cumulative sum (CUSUM) chart. In addition, new schemes have been proposed based on alternative weighting of past data, usually to put greater emphasis on past data and less weight on current and recent data. In other cases, the output of one process monitoring method, such as the EWMA statistic, is used as the input to another method, such as the CUSUM chart. Often the recursive formula for a control chart statistic is itself used recursively to form a new control chart statistic. We find the use of these ad hoc methods to be unjustified. Statistical performance comparisons justifying the use of these methods have been either flawed by focusing only on zero-state run length metrics or by making comparisons to an unnecessarily weak competitor.