75 citations · 199 across the 21 of their papers we have counts for
26 papers
Bounded-memory adjusted scores estimation in generalized linear models with large data sets
Patrick Zietkiewicz, Ioannis Kosmidis
The widespread use of maximum Jeffreys'-prior penalized likelihood in binomial-response generalized linear models, and in logistic regression, in particular, are supported by the r…
Maximum softly-penalized likelihood for mixed effects logistic regression
Philipp Sterzinger, Ioannis Kosmidis
Maximum likelihood estimation in logistic regression with mixed effects is known to often result in estimates on the boundary of the parameter space. Such estimates, which include…
Mean and median bias reduction: A concise review and application to adjacent-categories logit models
Ioannis Kosmidis
The estimation of categorical response models using bias-reducing adjusted score equations has seen extensive theoretical research and applied use. The resulting estimates have bee…
Scalable Marked Point Processes for Exchangeable and Non-Exchangeable Event Sequences
Aristeidis Panos, Ioannis Kosmidis, Petros Dellaportas
We adopt the interpretability offered by a parametric, Hawkes-process-inspired conditional probability mass function for the marks and apply variational inference techniques to der…
Flexible marked spatio-temporal point processes with applications to event sequences from association football
Santhosh Narayanan, Ioannis Kosmidis, Petros Dellaportas
We develop a new family of marked point processes by focusing the characteristic properties of marked Hawkes processes exclusively to the space of marks, providing the freedom to s…
Bias Reduction as a Remedy to the Consequences of Infinite Estimates in Poisson and Tobit Regression
Susanne Köll, Ioannis Kosmidis, Christian Kleiber +1
Data separation is a well-studied phenomenon that can cause problems in the estimation and inference from binary response models. Complete or quasi-complete separation occurs when…