Sinsomboonthong, Saichon

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School of Science

saichon.si@kmitl.ac.th

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Adjusted Optimal Trimmed Theil-Sen Method for Multiple Regression Model, with Outliers Detection and Management
(2025-01-01)
Sinsomboonthong, Juthaphorn
;
Phaeobang, Achara
;
Sinsomboonthong, Saichon
The objective of this research was to propose the adjusted optimal trimmed Theil-Sen (AOTS) method and compare the efficiency, with outliers, of four parametric and two nonparametric statistical point estimation methods for multiple regression analysis. The parametric methods consisted of the following: Ordinary Least Squares (OLS), Ordinary Least Trimmed Squares (OLTS), Parametric Bootstrap (PB), and Jackknife (JK) methods. The nonparametric methods consisted of Optimal Theil-Sen (OTS) and proposed AOTS methods. Data were simulated in a randomized manner in three instances of simulation: one, simulation of independent variables and errors without outliers; two, simulation of independent variables with outliers; and three, simulation of errors with outliers. Outliers were detected by an Interquartile Range (IQR) method. Both ends of the data were truncated to deal with outliers. Y-intercept and regression coefficient were estimated with six estimation methods. The measure for comparing the performances of these methods was a mean square error. For the parametric methods, when the independent variables had outliers with normal distribution, the PB method provided the least mean square error. It would be a good substitute for the OLS method. When the errors had outliers, for all normal, uniform, and gamma distributions, the performance of the OLTS method was better than the OLS method. For the nonparametric methods, when the independent variables had outliers with normal or uniform distributions, the proposed AOTS method performed better than the OTS method. In the same way, when the independent variables had outliers with gamma distribution, the proposed AOTS methods performed competitively to the OTS method. However, when the errors had outliers with normal, uniform, or gamma distribution, the OTS method edged over the proposed AOTS method.
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New adjusted missing value imputation in multiple regression with simple random sampling and rank set sampling methods
(2025-03-01)
Sinsomboonthong, Juthaphorn
;
Sinsomboonthong, Saichon
This research compared the efficiency of several adjusted missing value imputation methods in multiple regression analysis. The four imputation methods were the following: regression-ratio quartile1,3 (R-RQ1,3) imputation of Al-Omari, Jemain and Ibrahim; adjusted regression-chain ratio quartile1,3 (AR-CRQ1,3) imputation of Kadilar and Cinji; adjusted regression-multivariate ratio quatile1,3 (AR-MRQ1,3) imputation of Feng, Ni, and Zou; and adjusted regression-multivariate chain ratio quartile1,3 (AR-MCRQ1,3) imputation of Lu for each simple random sampling (SRS) and rank set sampling (RSS). The performance measures mean square error (MSE) and mean absolute percentage error (MAPE). The study showed that the AR-MRQ1 method with SRS provided the minimum mean square error for small error variance. However, the AR-MCRQ3 provided the minimum mean square error for a large error variance. Considering all error variance in mean absolute percentage error, the AR-MCRQ1 provided the minimum mean absolute percentage error. The AR-MRQ1 method with RSS provided the minimum mean square error for a small error variance. However, the AR-MCRQ3 provided the minimum mean square error for medium and large error variance. Regarding the mean absolute percentage error measure, the AR-MRQ1 provided the minimum mean absolute percentage error for a small error variance. However, the AR-MCRQ1 provided the minimum mean absolute percentage error for medium and large error variance. For both SRS and RSS, AR-MCRQ1 was the best method for missing value imputation in multiple regression analysis, followed by AR-MCRQ3. Moreover, the RSS estimators provided smaller MSE and MAPE than the SRS estimators. Therefore, the RSS estimators were more efficient than the SRS estimators.
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Performance Comparison of New Adjusted Min-Max with Decimal Scaling and Statistical Column Normalization Methods for Artificial Neural Network Classification
(2022-01-01)
Sinsomboonthong, Saichon
In this research, the normalization performance of the proposed adjusted min-max methods was compared to the normalization performance of statistical column, decimal scaling, adjusted decimal scaling, and min-max methods, in terms of accuracy and mean square error of the final classification outcomes. The evaluation process employed an artificial neural network classification on a large variety of widely used datasets. The best method was min-max normalization, providing 84.0187% average ranking of accuracy and 0.1097 average ranking of mean square error across all six datasets. However, the proposed adjusted-2 min-max normalization achieved a higher accuracy and a lower mean square error than min-max normalization on each of the following datasets: white wine quality, Pima Indians diabetes, vertical column, and Indian liver disease datasets. For example, the proposed adjusted-2 min-max normalization on white wine quality dataset achieved 100% accuracy and 0.00000282 mean square error. To conclude, for some classification applications on one of these specific datasets, the proposed adjusted-2 min-max normalization should be used over the other tested normalization methods because it performed better.
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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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Improved Wald Transformation p-Chart For Nonconforming Fraction
(2025-01-01)
Sinsomboonthong, Saichon
;
Sinsomboonthong, Juthaphorn
This study proposes a new improved transformation p-chart for nonconforming fraction of a production process, called improved Wald transformation p-chart. Via a simulation study, the efficiency of the proposed control chart was compared with the traditional p-chart, the improved square root transformation p-chart, and the Wilson p-chart. The simulation was conducted using the Monte Carlo technique for 180 situations and 10,000 times for each situation. The studied situations were as follows: the nonconforming fraction was set to be 0.01, 0.02, 0.05, 0.07, and 0.09; the shift of the nonconforming fraction was set to be 1.1, 1.3, 1.5, 2.0, 3.0, and 4.0; and the sample size (n) was set to be 30, 50, 100, 300, 500, and 1000 . The efficiency measures were out-of-control average run length and standard deviation of the run length. The results showed that the proposed chart was the most efficient among the four tested charts for a large sample size. In addition, the proposed chart tended to perform with the best efficiency for large sample sizes, n ≥ 500, and small nonconforming fraction below 0.1. It performed well with all the tested shifts of nonconforming fraction. However, the sensitivity to detect out-of-control items in the production process seemed to be same among the tested charts for smaller sample size, n < 500.
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Efficiency Comparison of New Adjusted Nonparametric and Parametric Statistics Interval Estimation Methods in the Simple Linear Regression Model
(2022-01-01)
Sinsomboonthong, Saichon
;
Sinsomboonthong, Juthaphorn
In this research, the authors were interested in an efficiency comparison study of new adjusted nonparametric and parametric statistics interval estimation methods in the simple linear regression model. The independent variable and the error came from normal, scale-contaminated normal, and gamma distributions. Six point estimations were performed, for example, least squares, Bayesian, Jack knife, Theil, optimum-type Theil, and new adjusted Theil-Sen and Siegel methods in the simple linear regression model with 1,000 iterations. The criteria used to consider in this study were the coefficient of the confidence interval and the average width of the confidence interval used to compare and determine the optimal effectiveness for six interval estimations of the simple linear regression model. In the interval estimation for normal and scale-contaminated normal distributions of β0, the least squares method had the narrowest average width of confidence interval. For the interval estimation of β1, the Bayesian method had the narrowest average width of confidence interval in a small variance of 1, followed by the same of optimum-type Theil and new adjusted Theil-Sen and Siegel methods, and Theil method, respectively. In the interval estimation for gamma distribution of β1, the Bayesian method had the narrowest average width of confidence interval, followed by optimum-type Theil, new adjusted Theil-Sen and Siegel, and Theil methods, respectively. The optimum-type Theil method was good for medium sample size, while Theil and new adjusted Theil-Sen and Siegel methods were good for small and large sample sizes. Therefore, new adjusted Theil-Sen and Siegel method can be used in many situations and can be used in place of optimum-type Theil and Theil methods for nonparametric statistics interval estimation.
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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.
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Robust Confidence Interval Estimation Method for the Mean of Poisson Distribution to Handle Outliers
(2022-01-01)
Sinsomboonthong, Juthaphorn
;
Sinsomboonthong, Saichon
In this article, the robust confidence interval estimation method for the Poisson mean to handle outliers in data set is presented. The proposed technique is called median bootstrap confidence interval or BC-Med method. The simulation study was constructed 160 situations to compare the efficiency for two criteria—the coverage probability and the average width—of five methods, namely, Wald, WaldC, Bégaud, Brown and BC-Med methods. It is found that in case of non-outliers in the data set, Brown method tends to have the desirable performance for almost all levels of the Poisson means and all sample sizes n. Furthermore, the efficiency of Wald method is also as good as that of Brown method for the sample sizes are not less than 30 and almost all levels of the Poisson means when data set is not contaminated with outliers. In case of outliers in the data set, BC-Med method tends to have the most efficiency for all levels of sample sizes n and almost all levels of the Poisson means. The findings will be useful for researchers to more accurately estimate the mean of Poisson distribution when sample data are contaminated with outliers, e.g., estimation of the number of deaths from accidents per day. Because the BC-Med method was developed from a robust location estimator, therefore outliers have slightly influence on the Poisson mean estimation for this proposed method.

Sinsomboonthong, Saichon

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