Vasicek quantile and mean regression models for bounded data: new formulation, mathematical derivations, and numerical applications

Basit Öğe Kaydı
dc.authorid0000-0003-3302-0705en_US
dc.contributor.authorMazucheli, Josmar
dc.contributor.authorAlves, Bruna
dc.contributor.authorKorkmaz, Mustafa Çağatay
dc.contributor.authorLeiva, Víctor
dc.date.accessioned2022-06-15T06:51:01Z
dc.date.available2022-06-15T06:51:01Z
dc.date.issued2022
dc.departmentAÇÜ, Eğitim Fakültesi, Eğitim Bilimleri Bölümüen_US
dc.description.abstractThe Vasicek distribution is a two-parameter probability model with bounded support on the open unit interval. This distribution allows for different and flexible shapes and plays an important role in many statistical applications, especially for modeling default rates in the field of finance. Although its probability density function resembles some well-known distributions, such as the beta and Kumaraswamy models, the Vasicek distribution has not been considered to analyze data on the unit interval, especially when we have, in addition to a response variable, one or more covariates. In this paper, we propose to estimate quantiles or means, conditional on covariates, assuming that the response variable is Vasicek distributed. Through appropriate link functions, two Vasicek regression models for data on the unit interval are formulated: one considers a quantile parameterization and another one its original parameterization. Monte Carlo simulations are provided to assess the statistical properties of the maximum likelihood estimators, as well as the coverage probability. An R package developed by the authors, named vasicekreg, makes available the results of the present investigation. Applications with two real data sets are conducted for illustrative purposes: in one of them, the unit Vasicek quantile regression outperforms the models based on the Johnson-SB, Kumaraswamy, unit-logistic, and unit-Weibull distributions, whereas in the second one, the unit Vasicek mean regression outperforms the fits obtained by the beta and simplex distributions. Our investigation suggests that unit Vasicek quantile and mean regressions can be of practical usage as alternatives to some well-known models for analyzing data on the unit interval.
dc.identifier.citationMazucheli, J., Alves, B., Korkmaz, M. Ç., & Leiva, V. (2022). Vasicek Quantile and Mean Regression Models for Bounded Data: New Formulation, Mathematical Derivations, and Numerical Applications. Mathematics, 10(9), 1389.en_US
dc.identifier.doi10.3390/math10091389
dc.identifier.issue9en_US
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://hdl.handle.net/11494/3839
dc.identifier.volume10en_US
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorKorkmaz, Mustafa Çağatay
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.relation.ispartofMathematics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMaximum likelihood methoden_US
dc.subjectMean regressionen_US
dc.subjectMonte Carlo simulationen_US
dc.subjectParametric quantile regressionen_US
dc.subjectR softwareen_US
dc.titleVasicek quantile and mean regression models for bounded data: new formulation, mathematical derivations, and numerical applicationsen_US
dc.typeArticle

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
Josmar_Mazucheli.pdf
Boyut:
984.33 KB
Biçim:
Adobe Portable Document Format
Açıklama:
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
[ X ]
İsim:
license.txt
Boyut:
1.44 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Eğitim Bilimleri Bölümü Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu