GAMLSS: a distributional regression approach

A tutorial of the generalized additive models for location, scale and shape (GAMLSS) is given here using two examples. GAMLSS is a general framework for performing regression analysis where not only the location (e.g., the mean) of the distribution but also the scale and shape of the distribution can be modelled by explanatory variables

Statistical methods for identifying anisotropy in the Spodoptera frugiperda spatial distribution

Corn is a very important agricultural product, however, some pests may cause damage to the corn productivity such as Spodoptera frugiperda, which prevents the plant from growing in a regular manner. Since the indiscriminate use of the pesticide may cause an increasing resistance of the insect besides an environmental damage, it is important to estimate the areas and the dominant directions where the insect may propagate. The main aim of this work was to study the spreading of the fall armyworm in a commercial agricultural area in the South of Brazil. For this, we considered a set including the location of each corn plant attacked by the insect. In particular, we assumed that the spatial locations given by the geographic coordinates constitute a spatial point pattern following a stationary Poisson point process. In order to detect the presence of possible dominant directions in the distribution of the fall armyworm infestation we studied the anisotropic features of the data by using some second-order spatial point-pattern analysis techniques such as the K directional test, the wavelet-based test, and the quadrat counting test. All the results showed that spatial distribution of fall armyworm may follow a clustered Poisson point process with the presence of an evident anisotropy mainly due to the shape and the distance between corn plants of the experimental area. These preliminary results could be used for reducing and optimizing the use of pesticides with a consequent decrease of the environmental impact

A multi‐analyses approach of inductive/deductive asymmetry in the affective priming paradigm

Rapidly evaluating our environment's beneficial and detrimental features is critical for our successful functioning. A classic paradigm used to investigate such fast and automatic evaluations is the affective priming (AP) paradigm, where participants classify valenced target stimuli (e.g., words) as good or bad while ignoring the valenced primes (e.g., words). We investigate the differential impact that verbs and adjectives used as primes and targets have on the AP paradigm. Based on earlier work on the Linguistic Category Model, we expect AP effect to be modulated by non-evaluative properties of the word stimuli, such as the linguistic category (e.g., if the prime is an adjective and the target is a verb versus the reverse). A reduction in the magnitude of the priming effect was predicted for adjective–verb prime-target pairs compared to verb–adjective prime-target pairs. Moreover, we implemented a modified crowdsourcing of statistical analyses implementing independently three different statistical approaches. Deriving our conclusions on the converging/ diverging evidence provided by the different approaches, we show a clear deductive/inductive asymmetry in AP paradigm (exp. 1), that this asymmetry does not require a focus on the evaluative dimension to emerge (exp. 2) and that the semantic-based asymmetry weakly extends to valence (exp. 3).info:eu-repo/semantics/publishedVersio

Principal component regression in GAMLSS applied to Greek-German government bond yield spreads

A solution to the problem of having to deal with a large number of interrelated explanatory variables within a generalized additive model for location, scale, and shape (GAMLSS) is given here using as an example the Greek-German government bond yield spreads from the 25th of April 2005 to the 31th of March 2010. Those were turbulent financial years, and in order to capture the spreads behaviour, a model has to be able to deal with the complex nature of the financial indicators used to predict the spreads. Fitting a model, using principal components regression of both main and first order interaction terms, for all the parameters of the assumed distribution of the response variable seems to produce promising results

Gaussian Markov random field spatial models in GAMLSS

This paper describes the modelling and fitting of Gaussian Markov random field spatial components within a Generalized Additive-Model for Location, Scale and Shape (GAMLSS) model. This allows modelling of any or all the parameters of the distribution for the response variable using explanatory variables and spatial effects. The response variable distribution is allowed to be a non-exponential family distribution. A new package developed in R to achieve this is presented. We use Gaussian Markov random fields to model the spatial effect in Munich rent data and explore some features and characteristics of the data. The potential of using spatial analysis within GAMLSS is discussed. We argue that the flexibility of parametric distributions, ability to model all the parameters of the distribution and diagnostic tools of GAMLSS provide an ideal environment for modelling spatial features of data

Association of the fibronectin type III domain–containing protein 5 rs1746661 single nucleotide polymorphism with reduced brain glucose metabolism in elderly humans

Fibronectin type III domain–containing protein 5 (FNDC5) and its derived hormone, irisin, have been associated with metabolic control in humans, with described FNDC5 single nucleotide polymorphisms being linked to obesity and metabolic syndrome. Decreased brain FNDC5/irisin has been reported in subjects with dementia due to Alzheimer’s disease. Since impaired brain glucose metabolism develops in ageing and is prominent in Alzheimer’s disease, here, we examined associations of a single nucleotide polymorphism in the FNDC5 gene (rs1746661) with brain glucose metabolism and amyloid-β deposition in a cohort of 240 cognitively unimpaired and 485 cognitively impaired elderly individuals from the Alzheimer’s Disease Neuroimaging Initiative. In cognitively unimpaired elderly individuals harbouring the FNDC5 rs1746661(T) allele, we observed a regional reduction in low glucose metabolism in memory-linked brain regions and increased brain amyloid-β PET load. No differences in cognition or levels of cerebrospinal fluid amyloid-β42, phosphorylated tau and total tau were observed between FNDC5 rs1746661(T) allele carriers and non-carriers. Our results indicate that a genetic variant of FNDC5 is associated with low brain glucose metabolism in elderly individuals and suggest that FNDC5 may participate in the regulation of brain metabolism in brain regions vulnerable to Alzheimer’s disease pathophysiology. Understanding the associations between genetic variants in metabolism-linked genes and metabolic brain signatures may contribute to elucidating genetic modulators of brain metabolism in humans