Sinsomboonthong, Saichon

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Sinsomboonthong, Saichon
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saichon.si@kmitl.ac.th

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    Item type:Publication,
    New quality control chart to quickly detect the changes of process average
    (2021-10-01)
    Sinsomboonthong, Juthaphorn
    ;
    Sinsomboonthong, Saichon
    The objective of this article is to propose a new control chart—improved exponentially weighted moving average (IEWMA) control chart—to fast detect the mean shifts of process when quality characteristic data are normally distributed. This chart still has robust property even though its controls limits are created from data with outliers. The efficiency inspection of IEWMA control chart is managed 504 situations for the simulation data. Moreover, the four control charts, namely, exponentially weighted moving average (EWMA), robust exponentially weighted moving average (REWMA), median mean absolute deviation (MDMAD), and average control charts, are compared the performances with IEWMA control chart. All charts are constructed by using data set in two cases, i.e., the first case that data are not include outliers and the second case that data are composed of outliers. It is found that in the case of non-outliers in the data, the three charts—IEWMA, EWMA and REWMA control charts—tend to have the most capability for process average shift detection for all sample sizes and all levels of the process average changes. For the case of outliers in the data, the IEWMA control chart tends to have the most efficiency for all sample sizes, especially for the tiny process shifts from the target.
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    Item type:Publication,
    Weighted maximum likelihood correlation coefficient to handle missing values and outliers in dataset
    (2021-01-01)
    Sinsomboonthong, Juthaphorn
    ;
    Sinsomboonthong, Saichon
    The proposed estimator, namely weighted maximum likelihood (WML) correlation coefficient, for measuring the relationship between two variables to concern about missing values and outliers in the dataset is presented. This estimator is proven by applying the conditional probability function to take care of some missing values and pay more attention to values near the center. However, outliers in the dataset are assigned a slight weight. These using techniques will give the robust proposed method when the preliminary assumptions are not met data analysis. To inspect about the quality of the proposed estimator, the six methods—WML, Pearson, median, percentage bend, biweight mid, and composite correlation coefficients—are compared the properties in two criteria, i.e. the bias and mean squared error, via the simulation study. The results of generated data are illustrated that the WML estimator seems to have the best performance to withstand the missing values and outliers in dataset, especially for the tiny sample size and large percentage of outliers regardless of missing data levels. However, for the massive sample size, the median correlation coefficient seems to have the good estimator when linear relationship levels between two variables are approximately over 0.4 irrespective of outliers and missing data levels.