ABSTRACT In multivariate regression estimation, the rate of convergence depends on the dimension of the regressor. This fact, known as the curse of the dimensionality, motivated several works. The additive model, introduced by Stone (10), offers an efficient response to this problem. In the setting of continuous time processes, using the marginal integration method, we obtain the quadratic conv...

In this paper, we investigate the identifiability of the additive index model, also known as projection pursuit regression. Although a flexible regression tool, additive index models can be hard to interpret in practice due to a lack of identifiability. As noted by Horowitz (1998), “it is an open question whether there are identifying restrictions that yield useful forms”, in reference to addit...

Variance partitioning methods, which are built upon multivariate statistics, have been widely applied in different taxa and habitats in community ecology. Here, I performed a literature review on the development and application of the methods, and then discussed the limitation of available methods and the difficulties involved in sampling schemes. The central goal of the work is then to propose...

Methods Species presence data were collected from records in the literature and private and public collections. Ecological niche factor analysis was performed to extract pseudo-absences (probable absences), which, together with presence data, were modelled using generalized additive models. The models were run twice. Initially we used only environmental variables, and thereafter additional spat...

As an extension of the traditional Land Use Regression (LUR) modelling, the generalized additive model (GAM) was developed in recent years to explore the non-linear relationships between PM2.5 concentrations and the factors impacting it. However, these studies did not consider the loss of information regarding predictor variables. To address this challenge, a generalized additive model combinin...

In distributional semantics studies, there is a growing attention in compositionally determining the distributional meaning of word sequences. Yet, compositional distributional models depend on a large set of parameters that have not been explored. In this paper we propose a novel approach to estimate parameters for a class of compositional distributional models: the additive models. Our approa...

Recent advances in functional Magnetic Resonance Imaging (fMRI) offer a significant new approach to studying semantic representations in humans by making it possible to directly observe brain activity while people comprehend words and sentences. In this study, we investigate how humans comprehend adjective-noun phrases (e.g. strong dog) while their neural activity is recorded. Classification an...

I compare two different ways of integrating mortality into life-cycle models: the standard additive model with time preferences, on the one hand, and a formulation that rules out the existence of time preference, but allows for risk aversion with respect to the length of life, on the other hand. These models are of similar complexity, but rather different in their fundamental assumptions. I sho...

Additive models for conditional quantile functions provide an attractive framework for nonparametric regression applications focused on features of the response beyond its central tendency. Total variation roughness penalities can be used to control the smoothness of the additive components much as squared Sobelev penalties are used for classical L2 smoothing splines. We describe a general appr...

The inherent intractability of probabilistic in­ ference has hindered the application of be­ lief networks to large domains. Noisy OR­ gates [30] and probabilistic similarity net­ works [18, 17) escape the complexity of infer­ ence by restricting model expressiveness. Re­ cent work in the application of belief-network models to time-series analysis and forecasting [9, 10) has given rise to the ...