ASSESSMENT OF HYPOTHESES TEST FOR THE COMPONENTS OF MEAN SQUARE ERROR OF PREDICTION
Main Article Content
Abstract
The analysis of mean square error of prediction is helpful to compare measured values with values simulated by mathematical models. Such analysis is based on the orthogonal decomposition of this quantity into three components which will indicate the probable constraints of the model concerning bias, unequal variance, and incomplete covariation when contrasted to actual values. However, such analysis has been carried out as a descriptive procedure without an adequate hypotheses test framework. Thus, we aimed to develop single hypothesis test to evaluate the statistical significance of mean square error of prediction components based on likelihood ratio test and χ² distribution. This proposal was evaluated by using simulated populations and was applied to a dataset obtained by simulating characteristics of cattle diets using two different models. We concluded that this test might help the modeler to focus on the real significant constraints of his model and to work on doing the necessary modifications on its mathematical structure in order to improve the accuracy and precision of the simulated values.
Article Details
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).