An effective approach towards efficient estimation of general linear model in case of heteroscedastic errors. Issue 2 (1st February 2023)

• Record Type: Journal Article
• Authors: Bhatti, Sajjad Haider; Khan, Faizan Wajid; Irfan, Muhammad; Raza, Muhammad Ali
• Is Part Of: Communications in statistics. Volume 52:Issue 2(2023)
• Page Start: 392
• Page End: 403
• Publication Date: 2023-02-01
• Subjects: Estimation -- False acceptance rate -- Heteroscedasticity -- Least squares ratio -- Mean absolute percentage error -- Regression -- Total mean square error; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2020.1856874
• Languages: English
• ISSNs: 0361-0918

Abstract

Abstract: Aiming at minimizing the ratio of error with respect to the response variable, the least squares ratio is a relatively new method for estimating the regression parameters. In the current article, the performance of this new approach is compared with the traditional OLS approach in the case when homoscedasticity of errors assumption is violated. A comparison is made through a simulation study using mean square error, mean absolute percentage error, and false acceptance rate as performance measures. It is observed that the least square ratio method outperforms the OLS method in case of moderate or severe heteroscedasticity for all sample sizes and in case of weak or mild heteroscedasticity for relatively small samples. Generally, it is noted that the efficiency of the least squares ratio method increases with an increase in the severity of heteroscedasticity as well as an increase in values of common error variance. The use of the least squares ratio method is recommended in case of mild or moderate to severe heteroscedasticity. Similar results were obtained from two real-life examples.