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A Simulation Study of Information Theoretic Techniques and Classical Hypothesis Tests in One Factor Anova

In: Proceedings of the First US/Japan Conference on the Frontiers of Statistical Modeling: An Informational Approach

Author

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  • Elizabeth P. Rosenblum

    (University of Virginia, Bureau of Educational Research)

Abstract

This work studies the performance of certain information theoretic techniques and two classical hypothesis testing procedures in identifying the correct models for population means in one factor ANOVA. A simulation study is used, and data samples are generated to conform to assumptions of the one factor fixed effects analysis of variance. Data sets with three data samples in each set are generated for different combinations of population means and sample sizes with equal variances for the samples. The sizes of the three samples are specified both equal and unequal. Three different specifications of population means are studied, all three means equal, two means equal and one mean different, and all three means different. The effectiveness of each statistical technique is measured by the empirical probabilities of selecting the correct model for the population means.

Suggested Citation

  • Elizabeth P. Rosenblum, 1994. "A Simulation Study of Information Theoretic Techniques and Classical Hypothesis Tests in One Factor Anova," Springer Books, in: Hamparsum Bozdogan & Stanley L. Sclove & Arjun K. Gupta & D. Haughton & G. Kitagawa & T. Ozaki & K. (ed.), Proceedings of the First US/Japan Conference on the Frontiers of Statistical Modeling: An Informational Approach, chapter 12, pages 319-346, Springer.
    • Handle: RePEc:spr:sprchp:978-94-011-0800-3_13
    DOI: 10.1007/978-94-011-0800-3_13
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