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Dynamic Spatial Pattern Recognition in Count Data

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New Developments in Statistical Modeling, Inference and Application

Part of the book series: ICSA Book Series in Statistics ((ICSABSS))

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Abstract

This study explores a Bayesian regression analysis for count data in the presence of spatial and temporal correlations. The contribution is to develop a regression model for count data that provides flexibility in modeling the complexity of zero-inflation, overdispersion, as well as spatial patterns in a dynamic manner. More importantly, it improves the computational efficiency via dimension reduction while handling the high-dimensional correlation structure in the data. The proposed model is applied to the survey data by the Northeast Fisheries Sciences Center (NEFSC) for estimation and prediction of the Atlantic cod in the Gulf of Maine—Georges Bank region. Both zero-inflated Poisson and negative binomial models are fitted. Model comparison shows the improvement in model fitting with consideration in the spatial-temporal correlation as well as the overdispersion in the count data.

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Acknowledgements

We thank two referees for their constructive comments and suggestions. Dr. Wang thanks the domestic and international conference travel support provided by the Charles Phelps Taft Center at the University of Cincinnati. Dr. Chen’s research was partially supported by NIH grants #GM 70335 and #P01 CA142538.

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Authors and Affiliations

  1. Department of Mathematical Sciences, University of Cincinnati, Cincinnati, OH, USA

    Xia Wang

  2. Department of Statistics, University of Connecticut, Storrs, CT, USA

    Ming-Hui Chen & Dipak K. Dey

  3. Joint Genome Institute, Lawrence Berkeley National Laboratory, Walnut Creek, CA, USA

    Rita C. Kuo

Authors
  1. Xia Wang
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  2. Ming-Hui Chen
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  3. Rita C. Kuo
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  4. Dipak K. Dey
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Corresponding author

Correspondence to Ming-Hui Chen .

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Editors and Affiliations

  1. Mailman School of Public Health Department of Biostatistics, Columbia University, New York, New York, USA

    Zhezhen Jin

  2. Division of Biostatistics, NYU School of Medicine, New York, New York, USA

    Mengling Liu

  3. Biostatistics Celgene Corporation, Summit, New Jersey, USA

    Xiaolong Luo

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Wang, X., Chen, MH., Kuo, R.C., Dey, D.K. (2016). Dynamic Spatial Pattern Recognition in Count Data. In: Jin, Z., Liu, M., Luo, X. (eds) New Developments in Statistical Modeling, Inference and Application. ICSA Book Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-42571-9_10

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