| dc.creator | Morales, Fidel Castro | |
| dc.creator | Lima Neto, Eufrásio de Andrade | |
| dc.date | 2022-10-11T20:14:16Z | |
| dc.date | 2022-10-11T20:14:16Z | |
| dc.date | 2014 | |
| dc.date.accessioned | 2023-09-04T12:35:55Z | |
| dc.date.available | 2023-09-04T12:35:55Z | |
| dc.identifier | MORALES, Fidel Castro; LIMA NETO, Eufrásio de Andrade. A bayesian approach for modeling interval-valued varaiables. Revista Brasileira de Biometria, São Paulo, v. 32, p. 360, 2014. Disponível em:http://jaguar.fcav.unesp.br/RME/fasciculos/v32/v32_n3/indice_v32_n3.php. Acesso em: 06 dez. 2017 | |
| dc.identifier | 0102-0811 | |
| dc.identifier | https://repositorio.ufrn.br/handle/123456789/49551 | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/8601372 | |
| dc.description | This paper proposes two Bayesian approaches to estimate the regression model
coefficients considering interval-valued variables as response and explanatory variables. The
first approach considers a more simple co-variance structure, while the second approach
supposes a more general co-variance structure. The posterior distribution for the parameters
was approximated considering Markov Chain Monte Carlo method (MCMC). A simulation study
is presented and suggests the effectiveness of the sampling scheme in recovering the true values
of the parameters and also indicates convergence of the parameter estimate algorithm. The new
approaches are applied to real interval-valued data sets and their performance compared. | |
| dc.format | application/pdf | |
| dc.language | pt_BR | |
| dc.publisher | Revista Brasileira de Biometria | |
| dc.rights | Acesso Aberto | |
| dc.subject | Interval variables | |
| dc.subject | Regression | |
| dc.subject | MCMC | |
| dc.subject | Bayesian approach | |
| dc.title | A bayesian approach for modeling interval-valued variables | |
| dc.type | article | |
