In additive models the problem of variable selection is strongly linked to the choice of the amount of smoothing used for components that represent metrical variables. Many software packages use separate toolsto solve the different tasks of variable selection and smoothing parameter choice. The...

Semi-parametric and nonparametric modeling and inference have been widely studied duringthe last two decades. In this manuscript, we do statistical inference based on semi-parametricand nonparametric models in several different scenarios.Firstly, we develop a semi-parametric additivity test for...

This article proposes a simple and fast approach to build simultaneous confi dence bands and perform specification tests for smooth curves in additive models. The method allows for handling of spatially heterogeneous functions and its derivatives as well as heteroscedasticity in the data. It is...

We consider theoretical bootstrap coupling techniques for nonparametric robust smoothers and quantile regression, and verify the bootstrap improvement. To cope with curse of dimensionality, a variant of coupling bootstrap techniques are developed for additive models with both symmetric error...

This paper explores the properties of using a generalized additive model with embedded variable selection for the prediction of bankruptcy. The main purpose is to explore an innovative way to close the gap between interpretation and prediction that has prevented widespread use of methods based...

We considered the problem of estimating effects in the following linear model for data arranged in a two-way table: Response = Common effect + Row effect + Column effect + Residual. This work was occasioned by a project to analyse Federal Aviation Administration (FAA) data on daily temporal...

This study considers the theoretical bootstrap “coupling” techniques for nonparametric robust smoothers and quantile regression, and we verify the bootstrap improvement. To handle the curse of dimensionality, a variant of “coupling” bootstrap techniques is developed for additive models...

This paper deals with the implementation of an additive linear model for mixture of mixtures including major and minor components. Experimental designs, derived from designs for qualitative factors, are built for the two classical cases of type A or type B mixtures. With such designs the...

Risk factor models in clinical epidemiology are important for identifying individuals at high risk of poor health outcomes and for guiding intervention strategies to reduce risk. Such models are often based on generalised linear models (GLM) with a multiplicative risk, rate or odds assumption....