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Monitoring the Spatial Correlation Among Functional Data Streams Through Moran’s Index Read chapter Read first chapter

2019 | OriginalPaper | Chapter

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

Authors : M. Subbiah, R. Renuka Devi, M. Gallo, M. R. Srinivasan

Published in: New Statistical Developments in Data Science

Publisher: Springer International Publishing

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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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Metadata
Title
Performance Comparison of Heterogeneity Measures for Count Data Models in Bayesian Perspective
Authors
M. Subbiah
R. Renuka Devi
M. Gallo
M. R. Srinivasan
Copyright Year
2019
Book
New Statistical Developments in Data Science

Print ISBN: 978-3-030-21157-8

Electronic ISBN: 978-3-030-21158-5

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-21158-5_13

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