Residuals vs Leverage plot for DB-GLM with Poisson response and Logarithmic link, using Gower’s distance and fitted taking into account the GCV method
This paper introduces local distance-based generalized linear models. These models extend (weighted) distance-based linear models firstly with the generalized linear model concept, then by localizing. Distances between individuals are the only predictor information needed to fit these models. Therefore they are applicable to mixed (qualitative and...
Previous studies have shown that significant gains can be achieved, when the parameters of the EY-NPMA protocol are updated on the fly. When EY-NPMA adapts dynamically to the offered load, improved figures both in throughput and access delay are observed. The estimation of the number of contending nodes, which is necessary for the calculation of th...
Predictions with distance-based linear and generalized linear models rely upon latent variables derived from the distance function. This key feature has the drawback of adding a non-linearity layer between observed predictors and response which shields one from the other and, in particular, prevents us from interpreting linear predictor coefficients as influence measures. In actuarial applications such as credit scoring or a priori rate-making we cannot forgo this capability, crucial to assess the relative leverage of risk factors. Towards the goal of recovering this functionality we define and study influence coefficients, measuring the relative importance of observed predictors. Unavoidably, due to inherent model non-linearities, these quantities will be local -valid in a neighborhood of a given point in predictor space.