Publikationen:
- Weiß, C.H., Homburg, A., Puig, P. (2016):
Testing for Zero Inflation and Overdispersion in INAR(1) Models.
Statistical Papers, 60(3), pp 823–848. - Homburg, A. (2019):
Criteria to Validate Count Data Model Selection.
Stochastic Models, Statistics and Their Applications 2019, pp 429-436. - Homburg, A., Weiß, C.H., Alwan, L.C., Frahm, G., Göb, R. (2019):
Evaluating Approximate Point Forecasting of Count Processes.
Econometrics 7(3), 30 (open access),
Special Issue „Discrete-Valued Time Series: Modelling, Estimation and Forecasting“. - Homburg, A. (2020):
Criteria for Evaluating Approximations of Count Distributions.
Communications in Statistics – Simulation and Computation, 49(12), pp 3152-3170. - Homburg, A., Weiß, C.H., Alwan, L.C., Frahm, G., Göb, R. (2021):
A Performance Analysis of Prediction Intervals for Count Time Series.
Journal of Forecasting, 40(4), pp 603-625. - Weiß, C.H., Testik, M.C., Homburg, A. (2021):
On the Design of Shewhart Control Charts for Count Time Series under Estimation Uncertainty.
Computers & Industrial Engineering, 157, 107331. - Homburg, A., Weiß, C.H., Frahm, G., Alwan, L.C., Göb, R. (2021):
Analysis and Forecasting of Risk in Count Processes.
Journal of Risk and Financial Management, 14(4), 182. - Weiß, C.H., Homburg, A., Alwan, L.C., Frahm, G., Göb, R.: Efficient Accounting for Estimation Uncertainty in Coherent Forecasting of Count Processes.
Journal of Applied Statistics 49(8), pp. 1957-1978, 2022. - Homburg, A., Weiß, C.H., Alwan, L.C., Frahm, G., Göb, R.: PMF-Forecasting for Count Processes: A Comprehensive Performance Analysis.
Theory and Applications of Time Series Analysis and Forecasting: Selected Contributions from ITISE 2021, Contributions to Statistics, Springer, pp. 79-90, 2023.
Letzte Änderung: 18. Februar 2026