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The performance of restricted AIC for irregular histogram models

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  • Sahika Gokmen
  • Johan Lyhagen

Abstract

Histograms are frequently used to perform a preliminary study of data, such as finding outliers and determining the distribution’s shape. It is common knowledge that choosing an appropriate number of bins is crucial to revealing the right information. It’s also well known that using bins of different widths, which called unequal bin width, is preferable to using bins of equal width if the bin width is selected carefully. However this is a much difficult issue. In this research, a novel approach to AIC for histograms with unequal bin widths was proposed. We demonstrate the advantage of the suggested approach in comparison to others using both extensive Monte Carlo simulations and empirical examples.

Suggested Citation

  • Sahika Gokmen & Johan Lyhagen, 2024. "The performance of restricted AIC for irregular histogram models," PLOS ONE, Public Library of Science, vol. 19(5), pages 1-15, May.
    • Handle: RePEc:plo:pone00:0289822
    DOI: 10.1371/journal.pone.0289822
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    References listed on IDEAS

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    1. Scott, David W. & Scott, Warren R., 2008. "Smoothed Histograms for Frequency Data on Irregular Intervals," The American Statistician, American Statistical Association, vol. 62, pages 256-261, August.
    2. Celisse, Alain & Robin, Stephane, 2008. "Nonparametric density estimation by exact leave-p-out cross-validation," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2350-2368, January.
    3. A. Azzalini & A.W. Bowman, 1990. "A Look at Some Data on the Old Faithful Geyser," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 39(3), pages 357-365, November.
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