Asymptotic results for multivariate local Whittle estimation with applications - Düker, Marie Christine; Pipiras, Vladas (2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings, 2019)
The asymptotic normality result is obtained for local Whittle estimators of all model parameters in a general formulation of multivariate long memory. The result is then used in devising a global statistical test for the so-called fractal non-connectivity, and in deriving the asymptotics of LASSO estimators of parameters in the so-called long-run variance matrix and its inverse. Some numerical illustrations are also provided.
CRIS-Team Scopus Importer:2023-04-20

Clustering future scenarios based on predicted range maps - Davidow, Matthew; Schafer, Toryn L. J.; Merow, Cory; Che-Castaldo, Judy; Düker, Marie-Christine; Feng, Emily; Matteson, David S. (Methods in Ecology and Evolution, 2023)
Predictions of biodiversity trajectories under climate change are crucial in order to act effectively in maintaining the diversity of species. In many ecological applications, future predictions are made under various global warming scenarios, as described by a range of different climate models. We propose a clustering methodology to synthesize and interpret the outputs of these various predictions. We propose an interpretable and flexible two-step methodology to measure the similarity between predicted species range maps and to cluster the future scenario predictions utilizing a spectral clustering technique. We implement and provide code for this method. We find that clustering based on predicted species range maps is mainly driven by the amount of warming rather than climate model or future scenario. We contrast this with clustering based only on predicted climate variables, which is driven primarily by climate models, that is, scenarios of the same climate model are clustered together, even when the amount of warming input to the models is varied. The differences between species-based and climate-based clusterings illustrate that it is crucial to incorporate ecological information to understand the relevant differences between climate models. Our findings can be used to better synthesize forecasts of biodiversity change under the wide spectrum of results that emerge when considering potential future scenarios.
CRIS-Team Scopus Importer:2023-04-20
Keywords: biodiversity; climate change; clustering; future scenarios; GCM; similarity measures

Risk Identification & Quantification in Complex Human-Natural Systems via Convergent Data Intensive Research - Schafer, Toryn L. J.; Mcgranaghan, Ryan M.; Sherman, Mila Getmansky; Feng, Mei-Ling E.; Owolabi, Olukunle O.; Ryan, Sean E.; Düker, Marie-Christine; Jauch, Michael; Matteson, David S. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2021)
Human-natural systems involve complex interdependent processes, but domain specific processes are traditionally studied in non-overlapping research silos. The Predictive Risk Investigation SysteM (PRISM) for multi-layer dynamic interconnection analysis is a group of collaborators across multiple domains who work to discover data driven connections specifically among domain risks. We bring our inter-disciplinary approach to risk assessment to our KDD'21 workshop. Our workshop is a step toward a holistic approach to systemic risk analysis by welcoming speakers in applied and technical research at the forefront of risk and complex systems.
CRIS-Team Scopus Importer:2023-04-20
Keywords: associated anomalies; complex systems; data-intensive risk assessment; human-natural systems; systemic risk; volatility

Vector AutoRegressive Moving Average Models: A Review - Düker, Marie-Christine; Matteson, David S.; Tsay, Ruey S.; Wilms, Ines (Wiley Interdisciplinary Reviews: Computational Statistics, 2025)
Vector AutoRegressive Moving Average (VARMA) models form a powerful and general model class for analyzing dynamics among multiple time series. While VARMA models encompass the Vector AutoRegressive (VAR) models, their popularity in empirical applications is dominated by the latter. Can this phenomenon be explained fully by the simplicity of VAR models? Perhaps many users of VAR models have not fully appreciated what VARMA models can provide. The goal of this review is to provide a comprehensive resource for researchers and practitioners seeking insights into the advantages and capabilities of VARMA models. We start by reviewing the identification challenges inherent to VARMA models thereby encompassing classical and modern identification schemes and we continue along the same lines regarding estimation, specification, and diagnosis of VARMA models. We then highlight the practical utility of VARMA models in terms of Granger Causality analysis, forecasting and structural analysis as well as recent advances and extensions of VARMA models to further facilitate their adoption in practice. Finally, we discuss some interesting future research directions where VARMA models can fulfill their potentials in applications as compared to their subclass of VAR models.
CRIS-Team Scopus Importer:2025-01-31
Keywords: forecasting; Granger causality; identification; model checking; multivariate time series