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A new log-location regression model: estimation, influence diagnostics and residual analysis

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Listed:
  • Rodrigo R. Pescim
  • Edwin M. M. Ortega
  • Gauss M. Cordeiro
  • Morad Alizadeh

Abstract

We introduce a log-linear regression model based on the odd log-logistic generalized half-normal distribution [7]. Some of its structural properties including explicit expressions for the density function, quantile and generating functions and ordinary moments are derived. We estimate the model parameters by the maximum likelihood method. For different parameter settings, proportion of censoring and sample size, some simulations are performed to investigate the behavior of the estimators. We derive the appropriate matrices for assessing local influence diagnostics on the parameter estimates under different perturbation schemes. We also define the martingale and modified deviance residuals to detect outliers and evaluate the model assumptions. In addition, we demonstrate that the extended regression model can be very useful in the analysis of real data and provide more realistic fits than other special regression models. The potentiality of the new regression model is illustrated by means of a real data set.

Suggested Citation

  • Rodrigo R. Pescim & Edwin M. M. Ortega & Gauss M. Cordeiro & Morad Alizadeh, 2017. "A new log-location regression model: estimation, influence diagnostics and residual analysis," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(2), pages 233-252, January.
  • Handle: RePEc:taf:japsta:v:44:y:2017:i:2:p:233-252
    DOI: 10.1080/02664763.2016.1168368
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    Cited by:

    1. M S Eliwa & Emrah Altun & Ziyad Ali Alhussain & Essam A Ahmed & Mukhtar M Salah & Hanan Haj Ahmed & M El-Morshedy, 2021. "A new one-parameter lifetime distribution and its regression model with applications," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-19, February.
    2. Emrah Altun & Haitham M. Yousof & G.G. Hamedani, 2018. "A new generalization of generalized half-normal distribution: properties and regression models," Journal of Statistical Distributions and Applications, Springer, vol. 5(1), pages 1-16, December.
    3. Prataviera, Fábio & Ortega, Edwin M.M. & Cordeiro, Gauss M. & Pescim, Rodrigo R. & Verssani, Bruna A.W., 2018. "A new generalized odd log-logistic flexible Weibull regression model with applications in repairable systems," Reliability Engineering and System Safety, Elsevier, vol. 176(C), pages 13-26.

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