<abstract xmlns="http://www.w3.org/1999/xhtml">

This paper describes an iterative analysis of incomplete genotype × environment data. <italic>L</italic><italic>2 environmental indices</italic> were introduced to enable the use of Joint Regression Analysis (JRA) in analyzing experiments with incomplete blocks. We now show how, once normality of yields is assumed, the introduction of L2 environmental indices provides a theoretical framework for Joint Regression Analysis. Using this framework, maximum likelihood estimators are obtained and likelihood ratio tests are derived. It is noted that the technique allows unequal weighting of data, and the special case of complete blocks is discussed.
</abstract>

This paper describes an iterative analysis of incomplete genotype × environment data. L2 environmental indices were introduced to enable the use of Joint Regression Analysis (JRA) in analyzing experiments with incomplete blocks. We now show how, once normality of yields is assumed, the introduction of L2 environmental indices provides a theoretical framework for Joint Regression Analysis. Using this framework, maximum likelihood estimators are obtained and likelihood ratio tests are derived. It is noted that the technique allows unequal weighting of data, and the special case of complete blocks is discussed.

Maximum Likelihood and L2 Environmental Indices in Joint Regression Analysis

Biometrical Letters

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Maximum Likelihood and L Environmental Indices in Joint Regression Analysis

This paper describes an iterative analysis of incomplete genotype × environment data. L2 environmental indices were introduced to enable the use of Joint...

Maximum Likelihood and L₂ Environmental Indices in Joint Regression Analysis

Publié en ligne: 30 juin 2022

Pages: 23 - 46

DOI: https://doi.org/10.2478/bile-2022-0003

Mots clés
Joint Regression Analysis, maximum likelihood estimators, likelihood ratio tests, L environmental indices

© 2022 Dulce Gamito Pereira, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Maximum Likelihood and L2 Environmental Indices in Joint Regression Analysis

Publié en ligne: 30 juin 2022

Pages: 23 - 46

DOI: https://doi.org/10.2478/bile-2022-0003

Mots clésJoint Regression Analysis, maximum likelihood estimators, likelihood ratio tests, L environmental indices

© 2022 Dulce Gamito Pereira, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Maximum Likelihood and L₂ Environmental Indices in Joint Regression Analysis

Mots clés
Joint Regression Analysis, maximum likelihood estimators, likelihood ratio tests, L environmental indices