| dc.creator | Ibacache-Pulgar, Germán | |
| dc.creator | Figueroa-Zúñiga, Jorge I. | |
| dc.creator | Marchant-Fuentes, Carolina | |
| dc.date | 2022-01-07T12:27:04Z | |
| dc.date | 2022-01-07T12:27:04Z | |
| dc.date | 2021 | |
| dc.date.accessioned | 2024-05-02T20:28:44Z | |
| dc.date.available | 2024-05-02T20:28:44Z | |
| dc.identifier | http://repositorio.ucm.cl/handle/ucm/3703 | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/9273990 | |
| dc.description | In this paper, we study a semiparametric additive beta regression model using a parameterization based on the mean and a dispersion parameter. This model is useful for situations where the response variable is continuous and restricted to the unit interval, in addition to being related to other variables through a semiparametric regression structure. First, we formulate the model and then estimation of its parameters is discussed. A back-fitting algorithm is derived to attain the maximum penalized
likelihood estimates by using natural cubic smoothing splines. We provide closed-form expressions for the score function, Fisher information matrix and its inverse. Local influence methods are derived as diagnostic tools. Finally, a practical illustration based real data is presented and discussed. | |
| dc.language | en | |
| dc.rights | Atribución-NoComercial-SinDerivadas 3.0 Chile | |
| dc.rights | http://creativecommons.org/licenses/by-nc-nd/3.0/cl/ | |
| dc.source | REVSTAT-Statistical Journal, 19(2), 255-274 | |
| dc.subject | Beta distribution | |
| dc.subject | Diagnostic techniques | |
| dc.subject | Maximum penalized likelihood estimates | |
| dc.subject | Penalized likelihood function | |
| dc.subject | Semiparametric additive models | |
| dc.title | Semiparametric additive beta regression models: Inference and local influence diagnostics | |
| dc.type | Article | |
