Ongoing Research Projects

Economics


• P2-13: Modeling Spatial Patterns in Stochastic Efficiency Frontiers of Health Care Services
• P3-8: Partial and Total Factor Productivity at Different Scales: Measurement, Decomposition, and Implications
• P3-9: Modelling Spatial Patterns and Network Effects in Efficiency Frontiers of Innovative Activity in Health Care
• P3-10: The Effect of the Regional Aggregation Scale in the Modelling of Agglomeration Effects
• A15: Patterns and determinants of nutrition and food insecurity

Ecology


• P2-5: Scaling problems in Estimation of Forest Biophysical Variables from Remote Sensing
• P2-6: Spatial scaling methods for ecological patterns
• P2-8: Understanding landscape moderation of biodiversity patterns
• P3-3: Scale Mismatch: A Major Challenge in Integrated Forest Monitoring, Combining Remote Sensing and Sample Based Field Observations
• P3-4: Spatial and Temporal Scaling of Biodiversity and Environment
• P3-5: Grid-Based Predictions of Biodiversity and Ecosystem Services at Different Spatial Scales
• P3-6: Biodiversity Patterns and Processes Across Landscapes Differing in Local Habitat Composition and Configuration
• P3-7: The Role of Heterogeneity in Spatial Plant Population Dynamics
• A5: Profiling plants to predict success and longevity of climate change-induced invasions
• A9: Modelling, management and restoration of grassland savannas in southern Africa
• A11: Land use patterns in Jambi
• A12 Agricultural biodiversity and associated services across rural-urban landscapes - modelling studies

Genetics


• P3-12: Integrative Association Studies of Complex Diseases in the Post GWAS Era
• P3-13: Understanding the Effective Number of Independent Chromosome Segments as a Tuning Parameter for Genomic Prediction
• P3-14: Dissecting Intraspecific Variation in Compound Eye Size in Drosophila Melanogaster via Integration of Genome, Transcriptome and Phenotype Data
• A8: Only Robust: Development of a SNP-Chip for the Genomic Selection of Metabolic-Robust Dairy Cows.
• A10: Integrating information on biochemical pathways in genomic prediction of phenotypes.
• A 13: Identifying biological pathways in expression data and modeling these pathways in the prediction equations
• A14: MAZE - Accessing the genomic and functional diversity of maize to improve quantitative traits
• A16: Kernel Methods for the Association Analysis of Genes and Networks including Environmental Components of Complex Diseases

Statistical Methodology


• PD-2: Modeling the dependence between extreme operational losses and economic covariates: statistical developments, model comparisons and empirical study based on conditional generalized Pareto regression.
• P3-1: Multilevel Generalized Additive Models for Location, Scale, and Shape
• P3-2: Uncertainty Quantification in Quantile Mixed Models