A stochastic frontier approach to survival analysis

In spite of the best set of covariates and statistical tools for the survival analysis, there are instances when experts do not rule out the existence of many non-observable factors that could influence the survival probability of an individual. The fact that every human body, sick or otherwise, strives to maximize time to death, renders the stochastic frontier analysis (vide 2 Kumbhakar, S. C. and Lovell, C. A.K. 2003. Stochastic Frontier Analysis, Cambridge: Cambridge University Press.  [Google Scholar]) as a meaningful tool to measure the unobservable individual-specific deficiency factor that accounts for the difference between the optimal and observed survival times. In this paper, given the survival data, an attempt is made to measure the deficiency factor for each individual in the data on adopting the stochastic frontier analysis. Such an attempt to quantify the effect of these unobservable factors can provide ample scope for further research in bio-medical studies. The utility of these estimates in the survival analysis is also highlighted using a real-life data.     

Forest structure and anthropogenic disturbances regulate plant invasion in urban forests

Urban forest ecosystems, the structure, and functions therein are subjected to anthropogenic disturbances. Native and sensitive species from those forests might be lost due to such disturbances. At the same time, supplemented anthropogenic resources might create opportunities for exotic and invasive species. Although invasive species are considered one of the major threats to the urban biodiversity and ecosystems, the research on invasion dynamics in the Himalayas has primarily focused on the impacts of invasion on forest structure and productivity. This study aims to understand the influence of forest structure and anthropogenic factors in invasion success that are poorly covered in the existing literature. We selected 11 urban forest patches for the study considering the presence-absence of selected invasive species and structural attributes. We used Principal Component Analysis (PCA) to reduce co-linearity in the covariates and generalized linear mixed effects model (GLMM) to identify the factors affecting invasion success. We found that forest structural attributes, namely, tree diameter, height and canopy cover, and anthropogenic disturbances regulate invasion success in urban forests. This implies that maintaining urban forest structural attributes, especially the stands with large-sized trees, is essential to control invasion in the context of urbanization.