Likelihood-based inference for the power regression model
In this paper we investigate an extension of the power-normal model, called the alpha-power model and specialize it to linear and nonlinear regression models, with and without correlated errors. Maximum likelihood estimation is considered with explicit derivation of the observed and expected Fisher...
| Authors: | ; ; |
|---|---|
| Format: | Article |
| Publication date: | 2015 |
| Country: | España |
| Institution: | Universitat Politècnica de Catalunya (UPC) |
| Repository: | UPCommons. Portal del coneixement obert de la UPC |
| Language: | English |
| OAI Identifier: | oai:upcommons.upc.edu:2117/88527 |
| Online Access: | https://hdl.handle.net/2117/88527 |
| Access Level: | Open access |
| Keyword: | Correlation maximum likelihood power-normal distribution regression. Classificació AMS::60 Probability theory and stochastic processes::60E Distribution theory Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica |
| Summary: | In this paper we investigate an extension of the power-normal model, called the alpha-power model and specialize it to linear and nonlinear regression models, with and without correlated errors. Maximum likelihood estimation is considered with explicit derivation of the observed and expected Fisher information matrices. Applications are considered for the Australian athletes data set and also to a data set studied in Xie et al. (2009). The main conclusion is that the proposed model can be a viable alternative in situations were the normal distribution is not the most adequate model. |
|---|