scholarly journals Exploring the Effect of Conversion on the Distribution of Inflectional Suffixes: A Multivariate Corpus Study

2021 ◽  
Vol 69 (3) ◽  
pp. 267-290
Author(s):  
Alexander Rauhut
Keyword(s):  
English Language ◽  
Generalized Additive Models ◽  
Statistical Modelling ◽  
Additive Models ◽  
Context Dependency ◽  
Word Class ◽  
Corpus Study ◽  
Productive Process ◽  
Lexical Similarity ◽  
Distributional Regression

Abstract Lexical ambiguity in the English language is abundant. Word-class ambiguity is even inherently tied to the productive process of conversion. Most lexemes are rather flexible when it comes to word class, which is facilitated by the minimal morphology that English has preserved. This study takes a multivariate quantitative approach to examine potential patterns that arise in a lexicon where verb-noun and noun-verb conversion are pervasive. The distributions of three inflectional suffixes, verbal -s, nominal -s, and -ed are explored for their interaction with degrees of verb-noun conversion. In order to achieve that, the lexical dispersion, context-dependency, and lexical similarity between the inflected and bare forms were taken into consideration and controlled for in a Generalized Additive Models for Location, Scale and Shape (GAMLSS; Stasinopoulos, M. D., R. A. Rigby, and F. De Bastiani. 2018. “GAMLSS: A Distributional Regression Approach.” Statistical Modelling 18 (3–4): 248–73). The results of a series of zero-one-inflated beta models suggest that there is a clear “uncanny” valley of lexemes that show similar proportions of verbal and nominal uses. Such lexemes have a lower proportion of inflectional uses when textual dispersion and context-dependency are controlled for. Furthermore, as soon as there is some degree of conversion, the probability that a lexeme is always encountered without inflection sharply rises. Disambiguation by means of inflection is unlikely to play a uniform role depending on the inflectional distribution of a lexeme.

scholarly journals Distributional Regression Techniques in Socioeconomic Research on the Inequality of Health with an Application on the Relationship between Mental Health and Income

2019 ◽  
Vol 16 (20) ◽  
pp. 4009 ◽  
Author(s):  
Alexander Silbersdorff ◽  
Kai Sebastian Schneider
Keyword(s):  
Mental Health ◽  
Health Inequalities ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Mental Health Score ◽  
Cross Sectional ◽  
Regression Techniques ◽  
German Socioeconomic Panel ◽  
The Relationship ◽  
Distributional Regression

This study addresses the much-discussed issue of the relationship between health and income. In particular, it focuses on the relation between mental health and household income by using generalized additive models of location, scale and shape and thus employing a distributional perspective. Furthermore, this study aims to give guidelines to applied researchers interested in taking a distributional perspective on health inequalities. In our analysis we use cross-sectional data of the German socioeconomic Panel (SOEP). We find that when not only looking at the expected mental health score of an individual but also at other distributional aspects, like the risk of moderate and severe mental illness, that the relationship between income and mental health is much more pronounced. We thus show that taking a distributional perspective, can add to and indeed enrich the mostly mean-based assessment of existent health inequalities.

Statistical modelling reveals spatial, temporal, and environmental preferences for white sharks at an oceanic aggregation site

2020 ◽  
Vol 655 ◽  
pp. 171-183
Author(s):  
EE Becerril-García ◽  
RO Martínez-Rincón ◽  
F Galván-Magaña ◽  
O Santana-Morales ◽  
EM Hoyos-Padilla
Keyword(s):  
Feeding Habits ◽  
Generalized Additive Models ◽  
Statistical Modelling ◽  
Time Of Day ◽  
White Shark ◽  
Additive Models ◽  
Environmental Preferences ◽  
Data Set ◽  
Aggregation Site ◽  
Guadalupe Island

Guadalupe Island, Mexico, is one of the most important white shark (Carcharodon carcharias) aggregation sites in the Eastern Pacific. In the waters surrounding Guadalupe Island, cage diving has been carried out since 2001 during August-November; however, there is scarce information regarding the factors associated with this seasonal aggregation. The purpose of this study was to describe the probability of occurrence of white sharks relative to spatial, temporal, and environmental factors in Guadalupe Island. Generalized additive models (GAMs) were used to describe the effect of sea surface temperature, water visibility, tide, moon phase, cloud cover, time of day, and location on white shark occurrence. GAMs were generated from a data set of 6266 sightings of white sharks, classified as immature males, mature males, immature females, and mature females. A sexual segregation related to month was observed, where females arrived after males during late September. GAMs evidenced a segregation of white sharks according to the analysed variables, which is consistent with previous observations in this locality. Environmental preferences for each white shark category are potentially influenced by feeding habits, sexual maturation, and reproduction. This study constitutes a baseline of the effect of the environment on the occurrence of white sharks in Guadalupe Island, which can be used in further studies regarding management and conservation in future climatic and anthropogenic scenarios. Its relevance is related to the understanding of its ecology in oceanic environments and the presence of this threatened species during the ecotourism season.

scholarly journals Interactively visualizing distributional regression models with distreg.vis

2021 ◽  
pp. 1471082X2110073
Author(s):  
Stanislaus Stadlmann ◽  
Thomas Kneib
Keyword(s):  
Regression Models ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Estimation Model ◽  
Response Distribution ◽  
Conditional Response ◽  
Link Functions ◽  
Covariate Effects ◽  
Parametric Response ◽  
Distributional Regression

A newly emerging field in statistics is distributional regression, where not only the mean but each parameter of a parametric response distribution can be modelled using a set of predictors. As an extension of generalized additive models, distributional regression utilizes the known link functions (log, logit, etc.), model terms (fixed, random, spatial, smooth, etc.) and available types of distributions but allows us to go well beyond the exponential family and to model potentially all distributional parameters. Due to this increase in model flexibility, the interpretation of covariate effects on the shape of the conditional response distribution, its moments and other features derived from this distribution is more challenging than with traditional mean-based methods. In particular, such quantities of interest often do not directly equate the modelled parameters but are rather a (potentially complex) combination of them. To ease the post-estimation model analysis, we propose a framework and subsequently feature an implementation in R for the visualization of Bayesian and frequentist distributional regression models fitted using the bamlss, gamlss and betareg R packages.

scholarly journals GAMLSS: A distributional regression approach

2018 ◽  
Vol 18 (3-4) ◽  
pp. 248-273 ◽  
Author(s):  
Mikis D Stasinopoulos ◽  
Robert A Rigby ◽  
Fernanda De Bastiani
Keyword(s):  
Regression Analysis ◽  
General Framework ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Explanatory Variables ◽  
Regression Approach ◽  
The Mean ◽  
Distributional Regression

Abstract: 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.

scholarly journals Predicting Malaria Transmission Dynamics in Dangassa, Mali: A Novel Approach Using Functional Generalized Additive Models

2020 ◽  
Vol 17 (17) ◽  
pp. 6339
Author(s):  
François Freddy Ateba ◽  
Manuel Febrero-Bande ◽  
Issaka Sagara ◽  
Nafomon Sogoba ◽  
Mahamoudou Touré ◽  
...  
Keyword(s):  
Wind Speed ◽  
Malaria Incidence ◽  
Generalized Additive Models ◽  
Community Health Center ◽  
Additive Model ◽  
Additive Models ◽  
Functional Regression ◽  
Novel Approach ◽  
Malaria Incidence Rate ◽  
Mean Wind Speed

Mali aims to reach the pre-elimination stage of malaria by the next decade. This study used functional regression models to predict the incidence of malaria as a function of past meteorological patterns to better prevent and to act proactively against impending malaria outbreaks. All data were collected over a five-year period (2012–2017) from 1400 persons who sought treatment at Dangassa’s community health center. Rainfall, temperature, humidity, and wind speed variables were collected. Functional Generalized Spectral Additive Model (FGSAM), Functional Generalized Linear Model (FGLM), and Functional Generalized Kernel Additive Model (FGKAM) were used to predict malaria incidence as a function of the pattern of meteorological indicators over a continuum of the 18 weeks preceding the week of interest. Their respective outcomes were compared in terms of predictive abilities. The results showed that (1) the highest malaria incidence rate occurred in the village 10 to 12 weeks after we observed a pattern of air humidity levels >65%, combined with two or more consecutive rain episodes and a mean wind speed <1.8 m/s; (2) among the three models, the FGLM obtained the best results in terms of prediction; and (3) FGSAM was shown to be a good compromise between FGLM and FGKAM in terms of flexibility and simplicity. The models showed that some meteorological conditions may provide a basis for detection of future outbreaks of malaria. The models developed in this paper are useful for implementing preventive strategies using past meteorological and past malaria incidence.

The Relationship between Mobility and COVID-19 in Germany: Modeling Case Occurrence using Apple's Mobility Trends Data

2021 ◽  
Author(s):  
Mark David Walker ◽  
Mihály Sulyok
Keyword(s):  
Generalized Additive Models ◽  
Information Criterion ◽  
Additive Models ◽  
Online Data ◽  
German Government ◽  
Explanatory Variables ◽  
Mobility Data ◽  
Community Mobility ◽  
The Relationship ◽  
Potential Use

Abstract Background Restrictions on social interaction and movement were implemented by the German government in March 2020 to reduce the transmission of coronavirus disease 2019 (COVID-19). Apple's “Mobility Trends” (AMT) data details levels of community mobility; it is a novel resource of potential use to epidemiologists. Objective The aim of the study is to use AMT data to examine the relationship between mobility and COVID-19 case occurrence for Germany. Is a change in mobility apparent following COVID-19 and the implementation of social restrictions? Is there a relationship between mobility and COVID-19 occurrence in Germany? Methods AMT data illustrates mobility levels throughout the epidemic, allowing the relationship between mobility and disease to be examined. Generalized additive models (GAMs) were established for Germany, with mobility categories, and date, as explanatory variables, and case numbers as response. Results Clear reductions in mobility occurred following the implementation of movement restrictions. There was a negative correlation between mobility and confirmed case numbers. GAM using all three categories of mobility data accounted for case occurrence as well and was favorable (AIC or Akaike Information Criterion: 2504) to models using categories separately (AIC with “driving,” 2511. “transit,” 2513. “walking,” 2508). Conclusion These results suggest an association between mobility and case occurrence. Further examination of the relationship between movement restrictions and COVID-19 transmission may be pertinent. The study shows how new sources of online data can be used to investigate problems in epidemiology.

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