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Performance Comparison of Heterogeneity Measures for Count Data Models in Bayesian Perspective

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New Statistical Developments in Data Science (SIS 2017)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 288))

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Abstract

Random effects model is one of the widely used statistical techniques in combining information from multiple independent studies and examine the heterogeneity. The present study has focussed on count data model which is comparatively uncommon in such research studies. Also the interest is to exploit the advantage of Bayesian modelling by incorporating plausible prior distributions on the parameter of interest. The study is illustrated with a data on rental bikes obtained from UC Irvine Machine Learning Repository. Results have indicated the impact of prior distributions and usage of heterogeneity estimators in count data models.

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Acknowledgements

Funding for this project was provided by 2017 funds of the University of Naples - L’Orientale (I).

Author information

Authors and Affiliations

  1. HCL Technologies Ltd, Chennai, India

    M. Subbiah

  2. Department of Statistics, Presidency College, Chennai, India

    R. Renuka Devi

  3. Department of Human and Social Sciences, University of Naples - L’Orientale, Naples, Italy

    M. Gallo

  4. Department of Statistics, University of Madras, Chennai, India

    M. R. Srinivasan

Authors
  1. M. Subbiah
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  2. R. Renuka Devi
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  3. M. Gallo
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  4. M. R. Srinivasan
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Corresponding author

Correspondence to M. Gallo .

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Editors and Affiliations

  1. Dipartimento di Statistica, Informatica, Applicazioni ‘G. Parenti’ (DiSIA), Università degli Studi di Firenze, Florence, Italy

    Alessandra Petrucci

  2. Dipartimento Scienze Statistiche, Sapienza Università di Roma, Rome, Italy

    Filomena Racioppi

  3. Dipartimento di Matematica e Fisica, Università della Campania ‘Luigi Vanvitelli’, Caserta, Italy

    Rosanna Verde

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Subbiah, M., Renuka Devi, R., Gallo, M., Srinivasan, M.R. (2019). Performance Comparison of Heterogeneity Measures for Count Data Models in Bayesian Perspective. In: Petrucci, A., Racioppi, F., Verde, R. (eds) New Statistical Developments in Data Science. SIS 2017. Springer Proceedings in Mathematics & Statistics, vol 288. Springer, Cham. https://doi.org/10.1007/978-3-030-21158-5_13

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