Kolloquium: Marie-Christine Düker (FAU): Antrittsvorlesung: High-Dimensional Latent Gaussian Count Time Series“

Antrittsvorlesung: High-Dimensional Latent Gaussian Count Time Series – Vortragende: Marie-Christine Düker – Einladender: T. Oertel

Abstract: The focus of this talk are stationary vector count time series
models defined via deterministic functions of a latent stationary vector
Gaussian series. The construction is very general and ensures a pre-specified
marginal distribution for the counts in each dimension, depending on unknown
parameters that can be marginally estimated. The vector Gaussian series
injects flexibility in the model’s temporal and cross-sectional dependencies,
perhaps through a parametric model akin to a vector autoregression. This talk
discusses how the latent Gaussian model can be estimated by relating the
covariances of the observed counts and the latent Gaussian series. In a
possibly high-dimensional setting, concentration bounds are established for the
differences between the estimated and true latent Gaussian autocovariances, in
terms of those for the observed count series and the estimated marginal
parameters. Applications of the result are given to the cases when the latent
Gaussian series either follows a vector autoregression or a factor model.