Adaptive Scheme for ANOVA Models

Main Article Content

Gilbert Biney
Gabriel Asare Okyere
Abukari Alhassan

Abstract

This paper deals with the concept of adaptive scheme and with an application to the Oneway ANOVA model under uncorrelated errors. Oneway ANOVA model is sensitive to nonnormality as well as variance heterogeneity. To overcome these problems, an adaptive scheme is proposed. The adaptive test is a two step procedure. The given data is first examined and classified based on measures of skewness and tailweight. Secondly, a selector statistic is used for selecting a test to be conducted. A 10,000 simulations were conducted to compare the performance of the two models from different continuous distributions. Analysis of real data sets on equal and unequal sample sizes were performed to evaluate the efficiency of the two models. The findings showed that our adaptive scheme outperformed the parametric F-test in symmetric or skewed distributions with varying tailweights except for symmetric and medium-tailed distributions.

Keywords:
Uncorrelated errors, adaptive test, selector statistic, skewness, tailweight, simulation, asymptotic relative efficiency (ARE).

Article Details

How to Cite
Biney, G., Okyere, G. A., & Alhassan, A. (2020). Adaptive Scheme for ANOVA Models. Journal of Advances in Mathematics and Computer Science, 35(4), 12-23. https://doi.org/10.9734/jamcs/2020/v35i430266
Section
Original Research Article

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