Flexible Univariate Count Models Based on Renewal Processes

Flexible univariate count models based on renewal processes. The models may include covariates and can be specified with familiar formula syntax as in glm() and 'flexsurv'.

Details

The methodology is described by Kharrat et al. (2019) . The paper is included in the package as vignette vignette('Countr_guide_paper', package = "Countr")).

The main function is renewalCount, see its documentation for examples.

Goodness of fit chi-square (likelihood ratio and Pearson) tests for glm and count renewal models are implemented in chiSq_gof and chiSq_pearson.

References

Baker R, Kharrat T (2017). “Event count distributions from renewal processes: fast computation of probabilities.” IMA Journal of Management Mathematics, 29(4), 415-433. ISSN 1471-678X, doi:10.1093/imaman/dpx008 , https://academic.oup.com/imaman/article-pdf/29/4/415/25693854/dpx008.pdf.

Boshnakov G, Kharrat T, McHale IG (2017). “A bivariate Weibull count model for forecasting association football scores.” International Journal of Forecasting, 33(2), 458--466.

Cameron AC, Trivedi PK (2013). Regression Analysis of Count Data, volume 53. Cambridge University Press.

Kharrat T, Boshnakov GN, McHale I, Baker R (2019). “Flexible Regression Models for Count Data Based on Renewal Processes: The Countr Package.” Journal of Statistical Software, 90(13), 1--35. doi:10.18637/jss.v090.i13 .

McShane B, Adrian M, Bradlow ET, Fader PS (2008). “Count models based on Weibull interarrival times.” Journal of Business & Economic Statistics, 26(3), 369--378.

Winkelmann R (1995). “Duration dependence and dispersion in count-data models.” Journal of Business & Economic Statistics, 13(4), 467--474.