arXiv subjects

Cornelius Fritz

Publications and source records attributed to Cornelius Fritz.

22 records · Page 2Linked to original sources

arXiv2021

Separable and Semiparametric Network-based Counting Processes applied to the International Combat Aircraft Trades

We propose a novel tie-oriented model for longitudinal event network data. The generating mechanism is assumed to be a multivariate Poisson process that governs the onset and repetition of yearly observed events with two separate intensity functions. We apply the model to a network obtained from the number of international deliveries of combat aircraft trades between 1950 and 2017. Based on a modified trade gravity approach we identify economic and political factors impeding or lightening the number of transfers. Extensive dynamics as well as country heterogeneity require the specification of semiparametric time-varying effects as well as random effects.

stat.AP↗

arXiv2021

Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany

During 2020, the infection rate of COVID-19 has been investigated by many scholars from different research fields. In this context, reliable and interpretable forecasts of disease incidents are a vital tool for policymakers to manage healthcare resources. Several experts have called for the necessity to account for human mobility to explain the spread of COVID-19. Existing approaches are often applying standard models of the respective research field. This habit, however, often comes along with certain restrictions. For instance, most statistical or epidemiological models cannot directly incorporate unstructured data sources, including relational data that may encode human mobility. In contrast, machine learning approaches may yield better predictions by exploiting these data structures, yet lack intuitive interpretability as they are often categorized as black-box models. We propose a trade-off between both research directions and present a multimodal learning approach that combines the advantages of statistical regression and machine learning models for predicting local COVID-19 cases in Germany. This novel approach enables the use of a richer collection of data types, including mobility flows and colocation probabilities, and yields the lowest MSE scores throughout our observational period in our benchmark study. The results corroborate the necessity of including mobility data and showcase the flexibility and interpretability of our approach.

cs.LG↗

arXiv2020

The Role of Governmental Weapons Procurements in Forecasting Monthly Fatalities in Intrastate Conflicts: A Semiparametric Hierarchical Hurdle Model

Accurate and interpretable forecasting models predicting spatially and temporally fine-grained changes in the numbers of intrastate conflict casualties are of crucial importance for policymakers and international non-governmental organisations (NGOs). Using a count data approach, we propose a hierarchical hurdle regression model to address the corresponding prediction challenge at the monthly PRIO-grid level. More precisely, we model the intensity of local armed conflict at a specific point in time as a three-stage process. Stages one and two of our approach estimate whether we will observe any casualties at the country- and grid-cell-level, respectively, while stage three applies a regression model for truncated data to predict the number of such fatalities conditional upon the previous two stages. Within this modelling framework, we focus on the role of governmental arms imports as a processual factor allowing governments to intensify or deter from fighting. We further argue that a grid cell's geographic remoteness is bound to moderate the effects of these military buildups. Out-of-sample predictions corroborate the effectiveness of our parsimonious and theory-driven model, which enables full transparency combined with accuracy in the forecasting process.

stat.AP↗

arXiv2019

Tempus Volat, Hora Fugit -- A Survey of Tie-Oriented Dynamic Network Models in Discrete and Continuous Time

Given the growing number of available tools for modeling dynamic networks, the choice of a suitable model becomes central. The goal of this survey is to provide an overview of tie-oriented dynamic network models. The survey is focused on introducing binary network models with their corresponding assumptions, advantages, and shortfalls. The models are divided according to generating processes, operating in discrete and continuous time. First, we introduce the Temporal Exponential Random Graph Model (TERGM) and the Separable TERGM (STERGM), both being time-discrete models. These models are then contrasted with continuous process models, focusing on the Relational Event Model (REM). We additionally show how the REM can handle time-clustered observations, i.e., continuous time data observed at discrete time points. Besides the discussion of theoretical properties and fitting procedures, we specifically focus on the application of the models on two networks that represent international arms transfers and email exchange. The data allow to demonstrate the applicability and interpretation of the network models.

cs.SI↗