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Article: Modeling linguistic variables with regression models: Addressing non-gaussian distributions, non-independent observations, and non-linear predictors with random effects and generalized additive models for location, scale, and shape

TitleModeling linguistic variables with regression models: Addressing non-gaussian distributions, non-independent observations, and non-linear predictors with random effects and generalized additive models for location, scale, and shape
Authors
KeywordsPhonemic inventory size
Smooth terms
Generalized additive models
Box-Cox t distribution
Delaporte distribution
Generalized linear models
GAMLSS
Mixed-effects models
Issue Date2018
Citation
Frontiers in Psychology, 2018, v. 9, n. APR How to Cite?
Abstract© 2018 Coupé. As statistical approaches are getting increasingly used in linguistics, attention must be paid to the choice of methods and algorithms used. This is especially true since they require assumptions to be satisfied to provide valid results, and because scientific articles still often fall short of reporting whether such assumptions are met. Progress is being, however, made in various directions, one of them being the introduction of techniques able to model data that cannot be properly analyzed with simpler linear regression models. We report recent advances in statistical modeling in linguistics. We first describe linear mixed-effects regression models (LMM), which address grouping of observations, and generalized linear mixed-effects models (GLMM), which offer a family of distributions for the dependent variable. Generalized additive models (GAM) are then introduced, which allow modeling non-linear parametric or non-parametric relationships between the dependent variable and the predictors. We then highlight the possibilities offered by generalized additive models for location, scale, and shape (GAMLSS). We explain how they make it possible to go beyond common distributions, such as Gaussian or Poisson, and offer the appropriate inferential framework to account for 'difficult' variables such as count data with strong overdispersion. We also demonstrate how they offer interesting perspectives on data when not only the mean of the dependent variable is modeled, but also its variance, skewness, and kurtosis. As an illustration, the case of phonemic inventory size is analyzed throughout the article. For over 1,500 languages, we consider as predictors the number of speakers, the distance from Africa, an estimation of the intensity of language contact, and linguistic relationships. We discuss the use of random effects to account for genealogical relationships, the choice of appropriate distributions to model count data, and non-linear relationships. Relying on GAMLSS, we assess a range of candidate distributions, including the Sichel, Delaporte, Box-Cox Green and Cole, and Box-Cox t distributions. We find that the Box-Cox t distribution, with appropriate modeling of its parameters, best fits the conditional distribution of phonemic inventory size. We finally discuss the specificities of phoneme counts, weak effects, and how GAMLSS should be considered for other linguistic variables.
Persistent Identifierhttp://hdl.handle.net/10722/262791
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorCoupé, Christophe-
dc.date.accessioned2018-10-08T02:47:03Z-
dc.date.available2018-10-08T02:47:03Z-
dc.date.issued2018-
dc.identifier.citationFrontiers in Psychology, 2018, v. 9, n. APR-
dc.identifier.urihttp://hdl.handle.net/10722/262791-
dc.description.abstract© 2018 Coupé. As statistical approaches are getting increasingly used in linguistics, attention must be paid to the choice of methods and algorithms used. This is especially true since they require assumptions to be satisfied to provide valid results, and because scientific articles still often fall short of reporting whether such assumptions are met. Progress is being, however, made in various directions, one of them being the introduction of techniques able to model data that cannot be properly analyzed with simpler linear regression models. We report recent advances in statistical modeling in linguistics. We first describe linear mixed-effects regression models (LMM), which address grouping of observations, and generalized linear mixed-effects models (GLMM), which offer a family of distributions for the dependent variable. Generalized additive models (GAM) are then introduced, which allow modeling non-linear parametric or non-parametric relationships between the dependent variable and the predictors. We then highlight the possibilities offered by generalized additive models for location, scale, and shape (GAMLSS). We explain how they make it possible to go beyond common distributions, such as Gaussian or Poisson, and offer the appropriate inferential framework to account for 'difficult' variables such as count data with strong overdispersion. We also demonstrate how they offer interesting perspectives on data when not only the mean of the dependent variable is modeled, but also its variance, skewness, and kurtosis. As an illustration, the case of phonemic inventory size is analyzed throughout the article. For over 1,500 languages, we consider as predictors the number of speakers, the distance from Africa, an estimation of the intensity of language contact, and linguistic relationships. We discuss the use of random effects to account for genealogical relationships, the choice of appropriate distributions to model count data, and non-linear relationships. Relying on GAMLSS, we assess a range of candidate distributions, including the Sichel, Delaporte, Box-Cox Green and Cole, and Box-Cox t distributions. We find that the Box-Cox t distribution, with appropriate modeling of its parameters, best fits the conditional distribution of phonemic inventory size. We finally discuss the specificities of phoneme counts, weak effects, and how GAMLSS should be considered for other linguistic variables.-
dc.languageeng-
dc.relation.ispartofFrontiers in Psychology-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectPhonemic inventory size-
dc.subjectSmooth terms-
dc.subjectGeneralized additive models-
dc.subjectBox-Cox t distribution-
dc.subjectDelaporte distribution-
dc.subjectGeneralized linear models-
dc.subjectGAMLSS-
dc.subjectMixed-effects models-
dc.titleModeling linguistic variables with regression models: Addressing non-gaussian distributions, non-independent observations, and non-linear predictors with random effects and generalized additive models for location, scale, and shape-
dc.typeArticle-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.3389/fpsyg.2018.00513-
dc.identifier.scopuseid_2-s2.0-85045691106-
dc.identifier.volume9-
dc.identifier.issueAPR-
dc.identifier.spagenull-
dc.identifier.epagenull-
dc.identifier.eissn1664-1078-
dc.identifier.isiWOS:000430124900001-

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