2013Advanced materials researchOpen access

Comparison of Model Selection Method Using Data from Classical Designed Experiment

Natthasurang Yasungnoen, Patchanok Srisuradetchai

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Abstract

Model selection procedures play important role in many researches especially quantitative research. . In several area of sciences, the analysis and model selection of experiments are often used and often contains two fundamental goals associated with the experimental response of interest which are to determine the best model. The way to address these goals is to implement a model selection procedure. Then, the objectives of this research are to determine whether or not the final models selected are in agreement or differ substantially across the three approaches to model selection: using Akaike’s Information Criterion, using a p-value criterion, and using a stepwise procedure.. Generally, results from these three models are usually compare to each other. All selected models are based on the heredity principle to design the possible model for each design. The actual data from literature, consisting of the 2x3 and 32 and 3x4 factorial designs are used to determine the final model. The results show that the P-Value WH and Stepwise methods give the highest percentage of matched model.

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Model selection procedures play important role in many researches especially quantitative research. . In several area of sciences, the analysis and model selection of experiments are often used and often contains two fundamental goals associated with the experimental response of interest which are to determine the best model. The way to address these goals is to implement a model selection procedure. Then, the objectives of this research are to determine whether or not the final models selected are in agreement or differ substantially across the three approaches to model selection: using Akaike’s Information Criterion, using a p-value criterion, and using a stepwise procedure.. Generally, results from these three models are usually compare to each other. All selected models are based on the heredity principle to design the possible model for each design. The actual data from literature, consisting of the 2x3 and 32 and 3x4 factorial designs are used to determine the final model. The results show that the P-Value WH and Stepwise methods give the highest percentage of matched model.

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Available abstract

Model selection procedures play important role in many researches especially quantitative research. . In several area of sciences, the analysis and model selection of experiments are often used and often contains two fundamental goals associated with the experimental response of interest which are to determine the best model. The way to address these goals is to implement a model selection procedure. Then, the objectives of this research are to determine whether or not the final models selected are in agreement or differ substantially across the three approaches to model selection: using Akaike’s Information Criterion, using a p-value criterion, and using a stepwise procedure.. Generally, results from these three models are usually compare to each other. All selected models are based on the heredity principle to design the possible model for each design. The actual data from literature, consisting of the 2x3 and 32 and 3x4 factorial designs are used to determine the final model. The results show that the P-Value WH and Stepwise methods give the highest percentage of matched model.

Key concepts: Akaike information criterion, Selection (genetic algorithm), Model selection, Bayesian information criterion, Computer science, Value (mathematics), Information Criteria, Experimental data

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