Forecasting Income of Overseas Thai Workers with Regression Coefficient Estimation Using Adjusted S-estimator

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นิธิภัทร กมลสุข


This research developed a new method of regression coefficient estimation by adjusting S-estimator which used DMST statistic developed from "Q" _"n" in cooperation with Euclidean Distance and Minimum Spanning Tree under the Prim’s Algorithm as the default value in finding QDMST weight. In the process of estimating regression coefficient by applying re-weighted least square method to acquire the equation for forecasting the income of Thai laborers working in foreign countries as stated in the database of the Department of Employment of Thailand, Thai Social Security Office, and Bank of Thailand. The simulative results from 540 scenarios derived from 5 sample sizes, percentage of 6 outliers, 6 parameters, and the characteristics of 3 distributions, namely, normal distribution, Gamma Distribution, and Weibul Distribution, indicated that there were 396 cases in which the Adjusted S-estimator proved to be more effective than S-estimator when focused on mean square error. In most cases, the sampling size was equal to 100 and 200. If 〖" X" 〗_"1i" represents the number of Thai laborers working in Taiwan, "X" _"2i" represents the number of Thai laborers working in Singapore, "X" _"3i" represents the number of Thai laborers working in the other foreign countries accordingly, the research results showed that these variables (〖" X" 〗_"1i" ,"X" _"2i" ,"X" _"3i" ) influenced the income of Thai laborers working overseas ("Y" ̂_"i" ) at the rate of 56.3 percent after considering the adjusted coefficient of determination and naming the coefficient of correlation equal to 0.75. The outcome of regression coefficient estimation by applying adjusted S-estimator as demonstrated is "Y" ̂_"i" = 9813.02816 -0.54163〖" X" 〗_"1i" -1.038199 "X" _"2i" + 0.10425 "X" _"3i" . The Adjusted S-estimator defines MSE equal to 2,678,240.568 and the percentage of MAPE equal to 27.603 while, the S-estimator defines MSE equal to 5,376,983.567and the percentage of MAPE equal to 32.6174.


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