Revistes Publicacions URV: SORT - Statistics and Operations Research Transactions> 2015

Likelihood-based inference for the power regression model

  • Identification data

    Identifier: RP:2430
    Handle: https://hdl.handle.net/20.500.11797/RP2430
    Authors:
    Gómez, Héctor W.Bolfarine, HelenoMartínez-Flórez, Guillermo
    Abstract:
    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.

  • Others:

    URV's Author/s: Gómez, Héctor W. Bolfarine, Heleno Martínez-Flórez, Guillermo
    Keywords: Correlation, maximum likelihood, power-normal distribution, regression.
    Abstract: 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.
    Journal publication year: 2015
    Publication Type: info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article

  • Keywords:

    Correlation, maximum likelihood, power-normal distribution, regression.

  • Documents:

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