A much-needed introduction to the field of discrete-valued time series, with a focus on count-
data time series Time series analysis is an essential tool in a wide array of fields, including …

We consider network autoregressive models for count data with a non-random
neighborhood structure. The main methodological contribution is the development of …

Bootstrapping INAR models Page 1 Bernoulli 25(3), 2019, 2359–2408 https://doi.org/10.3150/18-BEJ1057
Bootstrapping INAR models CARSTEN JENTSCH1 and CHRISTIAN H. WEISS2 1Technische …

When modelling unbounded counts, their marginals are often assumed to follow either
Poisson (Poi) or negative binomial (NB) distributions. To test such null hypotheses, we …

Positively (resp. negatively) associated point processes are a class of point processes that
induce attraction (resp. inhibition) between the points. As an important example …

This paper develops likelihood-based methods for estimation, inference, model selection,
and forecasting of continuous-time integer-valued trawl processes. The full likelihood of …

We study general nonlinear models for time series networks of integer and continuous-
valued data. The vector of high-dimensional responses, measured on the nodes of a known …

Sample quantiles are consistent estimators for the true quantile and satisfy central limit
theorems (CLTs) if the underlying distribution is continuous. If the distribution is discrete, the …

We propose methods for detecting structural changes in time series with discrete‐valued
observations. The detector statistics come in familiar L2‐type formulations incorporating the …

To test the null hypothesis of a Poisson marginal distribution, test statistics based on the
Stein–Chen identity are proposed. For a wide class of Poisson count time series, the …