Quantile regression, that is the prediction of conditional quantiles, has steadily gained importance in statistical modeling and financial applications. The authors introduce a new semiparametric quantile regression method based on sequentially fitting a likelihood optimal Dvine copula to given data resulting in highly flexible models with easily extractable conditional quantiles. As a subclass of regular vine copulas, D-vines enable the modeling of multivariate copulas in terms of bivariate building blocks, a so-called pair-copula construction (PCC). The proposed algorithm works fast and accurate even in high dimensions and incorporates an automatic variable selection by maximizing the conditional log-likelihood. Further, typical issues of quantile regression such as quantile crossing or transformations, interactions and collinearity of variables are automatically taken care of. In a simulation study the improved accuracy and saved computational time of the approach in comparison with established quantile regression methods is highlighted. An extensive financial application to international credit default swap (CDS) data including stress testing and Value-at-Risk (VaR) prediction demonstrates the usefulness of the proposed method.
Biogeosciences and Forestry Biogeosciences and Forestry Biodiversity conservation and wood production in a Natura 2000 Mediterranean forest. A trade-off evaluation focused on the occurrence of microhabitats Giovanni Santopuoli (1-2-3) , Marco di Cristofaro (2) , Daniel Kraus (4) , Andreas Schuck (5) , Bruno Lasserre (2) , Marco Marchetti (1-2) The most significant European forest-related strategies highlight the importance of multifunctional forests for human wellbeing, due to the provision of a wide range of goods and services. However, managing competing aims, such as timber production, economic drivers and biodiversity conservation is often difficult for practitioners. In order to assess the loss and gain of ecosystem services caused by forestry, trade-off evaluation has been increasingly used to aid decision-making. In this study, four silvicultural scenarios are simulated using the Marteloscope approach to evaluate the trade-offs between biodiversity conservation and timber production. Tree-related Microhabitats (TreMs) are used as a proxy to evaluate forest habitat value, while timber production is assessed by the number of harvested trees, biomass removal and economic income. This study takes an innovative approach by investigating TreMs using the Marteloscope in mixed Mediterranean forest. The main findings from this paper confirm that tree-related microhabitats can be considered ecological indicators effective in identifying important habitat trees, to assess forest habitat value and support tree marking for thinning operations and management.
1. The retention of trees bearing tree-related microhabitats (TreMs) has become an important means of conserving biodiversity in production forests. However, we lack estimates of TreM formation rates and evidence on factors driving TreM formation. 2. Based on the observation of 80,099 living trees from 19 species groups in Europe and Iran, we estimated the probability of TreM occurrence on trees and the associated rate of first TreM formation as a function of tree DBH, management, tree species group and random site effects. We built a separate model for each of 11 TreM groups. 3. The hazard rate of first TreM formation (defined as the probability of formation of a first TreM forming on a tree that is known to have none, during an infinitesimal DBH increment) increased with DBH for some TreM groups like breedingwoodpecker-hole, rot-hole or root-concavity, indicating an acceleration in TreM formation during tree growth. However, it decreased with DBH for TreM groups like bark-loss or dendrotelm, indicating slower formation on very large trees. Most TreM groups had reduced formation rates in managed forests (last logging less than 100 years ago) compared to unmanaged forests (no logging for at least 100 years), with the exception of dendrotelms. No general difference appeared | 493
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.