Abstract:Bank Competition in Indonesia increase due to good economic growth and the improvement of the social middle class in Indonesia. Increased bank raises the fierce competition between banks and internal banks themselves. This makes the management of the bank should work seriously to maintain its existence. In this case the assessment of the bank become very important in the banking business to survive in today's banking industry. This study was conducted to determine the competitive commercial banks operating in … Show more
“…The SVD will visualize the interaction terms graphically by Biplot, making the GEI analysis easier. Biplot analytics is a descriptive method with two dimensional that visualized the interaction of genotype and environment (Yasin & Rusgiyono, 2013).…”
The genotype by environment interaction (GEI) analysis was usually done by Additive Main Effects and Multiplicative Interaction (AMMI) model with Biplot features, and recently there was a Row Column Interaction Model (RCIM) alternatively. In the Biplot of genotype (G) and genotype by environment (GE) interactions, known as the GGE Biplot, the main effect of environment (E) was deleted, while the main effect of G and the interaction effect of GE is kept and combined. Subsequently, continuing our recent research of the robustness of the GGE Biplot in RCIM models, this paper aims to develop the GGE Biplot by RCIM model to analyze the GEI with outlying observations. We used the RCIM model with Asymptotic Laplace Distribution (ALD) that was applied on the simulated data with scattered and single environment outliers to evaluate the robustness of the GGE Biplot. In addition, the robustness was evaluated using the R-squared statistic of the Procrustes analysis. It is shown that the GGE Biplot of RCIM with the ALD family function provides better robustness than the Gaussian. A noticeable superiority of the GGE Biplot with RCIM ALD appeared as the percentage of single environment outliers reach the number of rows of the data matrix.
“…The SVD will visualize the interaction terms graphically by Biplot, making the GEI analysis easier. Biplot analytics is a descriptive method with two dimensional that visualized the interaction of genotype and environment (Yasin & Rusgiyono, 2013).…”
The genotype by environment interaction (GEI) analysis was usually done by Additive Main Effects and Multiplicative Interaction (AMMI) model with Biplot features, and recently there was a Row Column Interaction Model (RCIM) alternatively. In the Biplot of genotype (G) and genotype by environment (GE) interactions, known as the GGE Biplot, the main effect of environment (E) was deleted, while the main effect of G and the interaction effect of GE is kept and combined. Subsequently, continuing our recent research of the robustness of the GGE Biplot in RCIM models, this paper aims to develop the GGE Biplot by RCIM model to analyze the GEI with outlying observations. We used the RCIM model with Asymptotic Laplace Distribution (ALD) that was applied on the simulated data with scattered and single environment outliers to evaluate the robustness of the GGE Biplot. In addition, the robustness was evaluated using the R-squared statistic of the Procrustes analysis. It is shown that the GGE Biplot of RCIM with the ALD family function provides better robustness than the Gaussian. A noticeable superiority of the GGE Biplot with RCIM ALD appeared as the percentage of single environment outliers reach the number of rows of the data matrix.
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