Predictive vegetation mapping approach based on spectral data, DEM and generalized additive models

Ensemble classification based on generalized additive models

Generalized additive models (GAMs) are a generalization of generalized linear models (GLMs) and constitute a powerful technique which has successfully proven its ability to capture nonlinear relationships between explanatory variables and a response variable in many domains. In this paper, GAMs are proposed as base classifiers for ensemble learning. Three alternative ensemble strategies for bin...

Generalized Additive Models with Spatio-temporal Data

Generalized additive models (GAMs) have been widely used. While the procedure for fitting a generalized additive model to independent data has been well established, not as much work has been done when the data are correlated. The currently available methods are not completely satisfactory in practice. A new approach is proposed to fit generalized additive models with spatio-temporal data via t...

Generalized additive models for current status data.

Current status data arise in studies where the target measurement is the time of occurrence of some event, but observations are limited to indicators of whether or not the event has occurred at the time the sample is collected--only the current status of each individual with respect to event occurrence is observed. Examples of such data arise in several fields, including demography, epidemiolog...

Generalized additive models for cancer mapping with incomplete covariates.

Maps depicting cancer incidence rates have become useful tools in public health research, giving valuable information about the spatial variation in rates of disease. Typically, these maps are generated using count data aggregated over areas such as counties or census blocks. However, with the proliferation of geographic information systems and related databases, it is becoming easier to obtain...

Target setting in additive models with preferences and interval data