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

Rodrigo R. Pescim, Edwin M. M. Ortega, Gauss M. Cordeiro and Morad Alizadeh

Journal of Applied Statistics, 2017, vol. 44, issue 2, 233-252

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.

Date: 2017
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Citations: View citations in EconPapers (3)

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DOI: 10.1080/02664763.2016.1168368

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