Normal scale mixture copula marginal regression with Box-Cox symmetric distributions (2022)
- Authors:
- USP affiliated authors: FERRARI, SILVIA LOPES DE PAULA - IME ; MEDEIROS, RODRIGO MATHEUS ROCHA DE - IME
- Unidade: IME
- Subjects: ANÁLISE DE SÉRIES TEMPORAIS; DISTRIBUIÇÕES (PROBABILIDADE)
- Keywords: Clustered Data; Dependence Structures; Log-Symmetric Distributions; Working Correlation Matrix
- Language: Inglês
- Abstract: The class of the Box-Cox symmetric distributions was recently introduced in the statistical literature. The class provides a flexible modeling framework for univariate independent positive continuous data with different levels of skewness and tail-heaviness. Additionally, the relatively easy parameter interpretation makes it attractive for regression purposes. However, more general applications may involve correlated data, such as when observations have a temporal or spatial dependence. Based on Sklar’s Theorem, the copula theory provides an approach to modeling dependence through a function (named copula) which describes how the elements of a random vector are associated. Particularly, copulas generated by scale mixtures of normal distributions allow the bivariate associations to determine the dependence structure of the random vector entirely. Moreover, they also achieve positive and negative associations without restrictions on the data dimension. This work introduces a broad class of marginal regression models to analyze correlated positive continuous data with Box-Cox symmetric marginal distributions, where a normal scale mixture copula describes the dependence. Our approach resembles the joint modeling of univariate observations of the classical generalized estimating equations model. It is possible to select one of several association structures specified in terms of nonlinear response transformations, which provides flexibility in modeling independent observations, time series, longitudinal, clustered, or spatially correlated data.
- Imprenta:
- Source:
- Título: Livro de Resumos
- Conference titles: Simpósio Nacional de Probabilidade e Estatística - SINAPE
-
ABNT
MEDEIROS, Rodrigo Matheus Rocha de e FERRARI, Sílvia Lopes de Paula. Normal scale mixture copula marginal regression with Box-Cox symmetric distributions. 2022, Anais.. São Paulo: ABE, 2022. Disponível em: https://app.eventize.com.br/upload/004449/files/Sinape2022_FINAL.pdf. Acesso em: 10 mar. 2026. -
APA
Medeiros, R. M. R. de, & Ferrari, S. L. de P. (2022). Normal scale mixture copula marginal regression with Box-Cox symmetric distributions. In Livro de Resumos. São Paulo: ABE. Recuperado de https://app.eventize.com.br/upload/004449/files/Sinape2022_FINAL.pdf -
NLM
Medeiros RMR de, Ferrari SL de P. Normal scale mixture copula marginal regression with Box-Cox symmetric distributions [Internet]. Livro de Resumos. 2022 ;[citado 2026 mar. 10 ] Available from: https://app.eventize.com.br/upload/004449/files/Sinape2022_FINAL.pdf -
Vancouver
Medeiros RMR de, Ferrari SL de P. Normal scale mixture copula marginal regression with Box-Cox symmetric distributions [Internet]. Livro de Resumos. 2022 ;[citado 2026 mar. 10 ] Available from: https://app.eventize.com.br/upload/004449/files/Sinape2022_FINAL.pdf - Mean and Variance for Count Regression Models Based on Reparameterized Distributions
- Multivariate Box-Cox symmetric models generated by a normal scale mixture copula
- Bootstrap prediction intervals in beta regressions
- Improved score tests in symmetric linear regression models
- Adjusted likelihood inference in an elliptical multivariate errors-in-variables model
- Errors-in-variables beta regression models
- Mixed beta regression: A Bayesian perspective
- Multiplicative errors-in-variables beta regression
- Robust estimation in beta regression via maximum Lq-likelihood
- Bartlett-type corrections for some score tests in proper dispersion models
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