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Estimation of Receiver Operating Characteristic Surface Using Mixtures of Finite Polya Trees (MFPT)

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  • Ben Kiprono Koech

Abstract

Generalisation of Receiver operating characteristic (ROC) curve has become increasingly useful in evaluating the performance of diagnostic tests that have more than binary outcomes. While parametric approaches have been widely used over the years, the limitations associated with parametric assumptions often make it difficult to modelling the volume under surface for data that do not meet criteria under parametric distributions. As such, estimation of ROC surface using nonparametric approaches have been proposed to obtained insights on available data. One of the common approaches to non-parametric estimation is the use of Bayesian models where assumptions about priors can be made then posterior distributions obtained which can then be used to model the data. This study uses Polya tree priors where mixtures of Polya trees approach was used to model simulated three-way ROC data. The results of VUS estimation which is considered a suitable inference in evaluating performance of a diagnostic test, indicated that the mixtures of Polya trees model fitted well the ROC surface data. Further, the model performed relatively well compared to parametric and semiparametric models under similar assumptions.

Suggested Citation

  • Ben Kiprono Koech, 2021. "Estimation of Receiver Operating Characteristic Surface Using Mixtures of Finite Polya Trees (MFPT)," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 10(2), pages 1-18, March.
    • Handle: RePEc:ibn:ijspjl:v:10:y:2021:i:2:p:18
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    References listed on IDEAS

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    1. Luo, Jingqin & Xiong, Chengjie, 2012. "DiagTest3Grp: An R Package for Analyzing Diagnostic Tests with Three Ordinal Groups," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 51(i03).
    2. Hanson T. & Johnson W.O., 2002. "Modeling Regression Error With a Mixture of Polya Trees," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1020-1033, December.
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    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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