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dbEmpLikeGOF: An R Package for Nonparametric Likelihood Ratio Tests for Goodness-of-Fit and Two-Sample Comparisons Based on Sample Entropy

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  • Miecznikowski, Jeffrey
  • Vexler, Albert
  • Shepherd, Lori

Abstract

We introduce and examine dbEmpLikeGOF, an R package for performing goodness-of-fit tests based on sample entropy. This package also performs the two sample distribution comparison test. For a given vector of data observations, the provided function dbEmpLikeGOF tests the data for the proposed null distributions, or tests for distribution equality between two vectors of observations. The proposed methods represent a distribution-free density-based empirical likelihood technique applied to nonparametric testing. The proposed procedure performs exact and very efficient p values for each test statistic obtained from a Monte Carlo (MC) resampling scheme. Note by using an MC scheme, we are assured exact level α tests that approximate nonparametrically most powerful Neyman-Pearson decision rules. Although these entropy based tests are known in the theoretical literature to be very efficient, they have not been well addressed in statistical software. This article briefly presents the proposed tests and introduces the package, with applications to real data. We apply the methods to produce a novel analysis of a recently published dataset related to coronary heart disease.

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  • Miecznikowski, Jeffrey & Vexler, Albert & Shepherd, Lori, 2013. "dbEmpLikeGOF: An R Package for Nonparametric Likelihood Ratio Tests for Goodness-of-Fit and Two-Sample Comparisons Based on Sample Entropy," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 54(i03).
    • Handle: RePEc:jss:jstsof:v:054:i03
    DOI: http://hdl.handle.net/10.18637/jss.v054.i03
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    Cited by:

    1. Hadi Alizadeh Noughabi & Albert Vexler, 2016. "An efficient correction to the density-based empirical likelihood ratio goodness-of-fit test for the inverse Gaussian distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(16), pages 2988-3003, December.
    2. Pavia, Jose M., 2015. "Testing Goodness-of-Fit with the Kernel Density Estimator: GoFKernel," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 66(c01).
    3. Bagkavos, Dimitrios & Patil, Prakash N., 2023. "Goodness-of-fit testing for normal mixture densities," Computational Statistics & Data Analysis, Elsevier, vol. 188(C).
    4. Hart, Jeffrey D. & Choi, Taeryon & Yi, Seongbaek, 2016. "Frequentist nonparametric goodness-of-fit tests via marginal likelihood ratios," Computational Statistics & Data Analysis, Elsevier, vol. 96(C), pages 120-132.

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