Author details

Fan Tang<sup>1</sup> and Joseph E. Cavanaugh<sup>2</sup> \*


### References


Author details

150 Time Series Analysis and Applications

References

Fan Tang<sup>1</sup> and Joseph E. Cavanaugh<sup>2</sup>

\*

2 Department of Biostatistics, College of Public Health, University of Iowa, Iowa City, IA, USA

[1] Lambert D. Zero-inflated Poisson regression, with an application to defects in manufactur-

[2] Yau KKW, Lee AH. Zero-inflated Poisson regression with random effects to evaluate an occupational injury prevention programme. Statistics in Medicine. 2001;20:2907-2920

[3] Min Y, Agresti A. Random effect models for repeated measures of zero-inflated count

[4] Yau KKW, Lee AH, Carrivick PJW. Modeling zero-inflated count series with application to occupational health. Computer Methods and Programs in Biomedicine. 2004;74:47-52

[5] Yang M, Zamba GKD, Cavanaugh JE. State-space models for count time series with

[6] Hall DB. Zero-inflated Poisson and binomial regression with random effects: A case study.

[7] Yang M, Zamba GKD, Cavanaugh JE. Markov regression models for count time series with excess zeros: A partial likelihood approach. Statistical Methodology. 2013;14:26-38

[8] Cox DR. Statistical analysis of time series: Some recent developments. Scandinavian

[9] Zeger SL, Qaqish B. Markov regression models for time series: A quasi-likelihood approach.

[10] Slud E, Kedem B. Partial likelihood analysis of logistic regression and autoregression.

[11] Davis RA, Dunsmuir WTM, Streett SB. Observation-driven models for Poisson counts.

[12] Freeland RK, McCabe BPM. Analysis of low count time series data by Poisson auto-

[13] Fokianos K, Kedem B. Partial likelihood inference for time series following generalized

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linear models. Journal of Time Series Analysis. 2004;25:173-197

\*Address all correspondence to: joe-cavanaugh@uiowa.edu

1 Genentech Inc., South San Francisco, CA, USA

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**Provisional chapter**
